2017
|
Emeršič, Žiga; Štruc, Vitomir; Peer, Peter Ear recognition: More than a survey Journal Article In: Neurocomputing, vol. 255, pp. 26–39, 2017. @article{emervsivc2017ear,
title = {Ear recognition: More than a survey},
author = {Žiga Emeršič and Vitomir Štruc and Peter Peer},
url = {https://arxiv.org/pdf/1611.06203.pdf},
year = {2017},
date = {2017-01-01},
journal = {Neurocomputing},
volume = {255},
pages = {26--39},
publisher = {Elsevier},
abstract = {Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community.},
keywords = {AWE, biometrics, dataset, ear, ear recognition, performance evalution, survey, toolbox},
pubstate = {published},
tppubtype = {article}
}
Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community. |
Meden, Blaž; Malli, Refik Can; Fabijan, Sebastjan; Ekenel, Hazim Kemal; Štruc, Vitomir; Peer, Peter Face deidentification with generative deep neural networks Journal Article In: IET Signal Processing, vol. 11, no. 9, pp. 1046–1054, 2017. @article{meden2017face,
title = {Face deidentification with generative deep neural networks},
author = {Blaž Meden and Refik Can Malli and Sebastjan Fabijan and Hazim Kemal Ekenel and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Face_Deidentification_with_Generative_Deep_Neural_Networks.pdf},
year = {2017},
date = {2017-01-01},
journal = {IET Signal Processing},
volume = {11},
number = {9},
pages = {1046--1054},
publisher = {IET},
abstract = {Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.},
keywords = {biometrics, computer vision, deidentification, face, privacy protection},
pubstate = {published},
tppubtype = {article}
}
Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective. |
Meden, Blaz; Emersic, Ziga; Struc, Vitomir; Peer, Peter k-Same-Net: Neural-Network-Based Face Deidentification Proceedings Article In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1–7, IEEE 2017. @inproceedings{meden2017kappa,
title = {k-Same-Net: Neural-Network-Based Face Deidentification},
author = {Blaz Meden and Ziga Emersic and Vitomir Struc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/k-same-net.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI)},
pages = {1--7},
organization = {IEEE},
abstract = {An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent generative neural networks (GNNs) with the well-known k-anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for dedentification by seamlessly combining features of identities used to train the GNN mode. furthermore, it allows us to guide the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comparative experiments with competing techniques on the XM2VTS dataset and discuss the main characteristics of our approach.},
keywords = {deidentification, face, privacy protection},
pubstate = {published},
tppubtype = {inproceedings}
}
An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent generative neural networks (GNNs) with the well-known k-anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for dedentification by seamlessly combining features of identities used to train the GNN mode. furthermore, it allows us to guide the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comparative experiments with competing techniques on the XM2VTS dataset and discuss the main characteristics of our approach. |
Das, Abhijit; Pal, Umapada; Ferrer, Miguel A; Blumenstein, Michael; Štepec, Dejan; Rot, Peter; Emeršič, Ziga; Peer, Peter; Štruc, Vitomir; Kumar, SV Aruna; S, Harish B SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition Proceedings Article In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 742–747, IEEE 2017. @inproceedings{das2017sserbc,
title = {SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition},
author = {Abhijit Das and Umapada Pal and Miguel A Ferrer and Michael Blumenstein and Dejan Štepec and Peter Rot and Ziga Emeršič and Peter Peer and Vitomir Štruc and SV Aruna Kumar and Harish B S},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/SSERBC2017.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE International Joint Conference on Biometrics (IJCB)},
pages = {742--747},
organization = {IEEE},
abstract = {This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject.
In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) eyes. A manual segmentation mask of these images was created to baseline both tasks.
Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and periocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems.
The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject},
keywords = {competition, sclera, sclera segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject.
In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) eyes. A manual segmentation mask of these images was created to baseline both tasks.
Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and periocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems.
The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject |
Grm, Klemen; Štruc, Vitomir; Artiges, Anais; Caron, Matthieu; Ekenel, Hazim K. Strengths and weaknesses of deep learning models for face recognition against image degradations Journal Article In: IET Biometrics, vol. 7, no. 1, pp. 81–89, 2017. @article{grm2017strengths,
title = {Strengths and weaknesses of deep learning models for face recognition against image degradations},
author = {Klemen Grm and Vitomir Štruc and Anais Artiges and Matthieu Caron and Hazim K. Ekenel},
url = {https://arxiv.org/pdf/1710.01494.pdf},
year = {2017},
date = {2017-01-01},
journal = {IET Biometrics},
volume = {7},
number = {1},
pages = {81--89},
publisher = {IET},
abstract = {Convolutional neural network (CNN) based approaches are the state of the art in various computer vision tasks including face recognition. Considerable research effort is currently being directed toward further improving CNNs by focusing on model architectures and training techniques. However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce. In this paper, we try to fill this gap and study the effects of different covariates on the verification performance of four recent CNN models using the Labelled Faces in the Wild dataset. Specifically, we investigate the influence of covariates related to image quality and model characteristics, and analyse their impact on the face verification performance of different deep CNN models. Based on comprehensive and rigorous experimentation, we identify the strengths and weaknesses of the deep learning models, and present key areas for potential future research. Our results indicate that high levels of noise, blur, missing pixels, and brightness have a detrimental effect on the verification performance of all models, whereas the impact of contrast changes and compression artefacts is limited. We find that the descriptor-computation strategy and colour information does not have a significant influence on performance.},
keywords = {CNN, convolutional neural networks, face recognition, googlenet, study, vgg},
pubstate = {published},
tppubtype = {article}
}
Convolutional neural network (CNN) based approaches are the state of the art in various computer vision tasks including face recognition. Considerable research effort is currently being directed toward further improving CNNs by focusing on model architectures and training techniques. However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce. In this paper, we try to fill this gap and study the effects of different covariates on the verification performance of four recent CNN models using the Labelled Faces in the Wild dataset. Specifically, we investigate the influence of covariates related to image quality and model characteristics, and analyse their impact on the face verification performance of different deep CNN models. Based on comprehensive and rigorous experimentation, we identify the strengths and weaknesses of the deep learning models, and present key areas for potential future research. Our results indicate that high levels of noise, blur, missing pixels, and brightness have a detrimental effect on the verification performance of all models, whereas the impact of contrast changes and compression artefacts is limited. We find that the descriptor-computation strategy and colour information does not have a significant influence on performance. |
Šket, Robert; Treichel, Nicole; Kublik, Susanne; Debevec, Tadej; Eiken, Ola; Mekjavić, Igor; Schloter, Michael; Vital, Marius; Chandler, Jenna; Tiedje, James M; Murovec, Boštjan; Prevoršek, Zala; Likar, Matevž; Stres, Blaž Hypoxia and inactivity related physiological changes precede or take place in absence of significant rearrangements in bacterial community structure: The PlanHab randomized trial pilot study Journal Article In: PLOS ONE, vol. 12, no. 12, pp. 1-26, 2017. @article{10.1371/journal.pone.0188556,
title = {Hypoxia and inactivity related physiological changes precede or take place in absence of significant rearrangements in bacterial community structure: The PlanHab randomized trial pilot study},
author = {Robert Šket and Nicole Treichel and Susanne Kublik and Tadej Debevec and Ola Eiken and Igor Mekjavić and Michael Schloter and Marius Vital and Jenna Chandler and James M Tiedje and Boštjan Murovec and Zala Prevoršek and Matevž Likar and Blaž Stres},
url = {https://doi.org/10.1371/journal.pone.0188556},
doi = {10.1371/journal.pone.0188556},
year = {2017},
date = {2017-01-01},
journal = {PLOS ONE},
volume = {12},
number = {12},
pages = {1-26},
publisher = {Public Library of Science},
abstract = {We explored the assembly of intestinal microbiota in healthy male participants during the randomized crossover design of run-in (5 day) and experimental phases (21-day normoxic bed rest (NBR), hypoxic bed rest (HBR) and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, with balanced fluid and dietary intakes, controlled circadian rhythm, microbial ambiental burden and 24/7 medical surveillance. The fraction of inspired O2 (FiO2) and partial pressure of inspired O2 (PiO2) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4000 m simulated altitude), respectively. A number of parameters linked to intestinal environment such as defecation frequency, intestinal electrical conductivity (IEC), sterol and polyphenol content and diversity, indole, aromaticity and spectral characteristics of dissolved organic matter (DOM) were measured (64 variables). The structure and diversity of bacterial microbial community was assessed using 16S rRNA amplicon sequencing. Inactivity negatively affected frequency of defecation and in combination with hypoxia increased IEC (p < 0.05). In contrast, sterol and polyphenol diversity and content, various characteristics of DOM and aromatic compounds, the structure and diversity of bacterial microbial community were not significantly affected over time. A new in-house PlanHab database was established to integrate all measured variables on host physiology, diet, experiment, immune and metabolic markers (n = 231). The observed progressive decrease in defecation frequency and concomitant increase in IEC suggested that the transition from healthy physiological state towards the developed symptoms of low magnitude obesity-related syndromes was dose dependent on the extent of time spent in inactivity and preceded or took place in absence of significant rearrangements in bacterial microbial community. Species B. thetaiotamicron, B. fragilis, B. dorei and other Bacteroides with reported relevance for dysbiotic medical conditions were significantly enriched in HBR, characterized with most severe inflammation symptoms, indicating a shift towards host mucin degradation and proinflammatory immune crosstalk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We explored the assembly of intestinal microbiota in healthy male participants during the randomized crossover design of run-in (5 day) and experimental phases (21-day normoxic bed rest (NBR), hypoxic bed rest (HBR) and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, with balanced fluid and dietary intakes, controlled circadian rhythm, microbial ambiental burden and 24/7 medical surveillance. The fraction of inspired O2 (FiO2) and partial pressure of inspired O2 (PiO2) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4000 m simulated altitude), respectively. A number of parameters linked to intestinal environment such as defecation frequency, intestinal electrical conductivity (IEC), sterol and polyphenol content and diversity, indole, aromaticity and spectral characteristics of dissolved organic matter (DOM) were measured (64 variables). The structure and diversity of bacterial microbial community was assessed using 16S rRNA amplicon sequencing. Inactivity negatively affected frequency of defecation and in combination with hypoxia increased IEC (p < 0.05). In contrast, sterol and polyphenol diversity and content, various characteristics of DOM and aromatic compounds, the structure and diversity of bacterial microbial community were not significantly affected over time. A new in-house PlanHab database was established to integrate all measured variables on host physiology, diet, experiment, immune and metabolic markers (n = 231). The observed progressive decrease in defecation frequency and concomitant increase in IEC suggested that the transition from healthy physiological state towards the developed symptoms of low magnitude obesity-related syndromes was dose dependent on the extent of time spent in inactivity and preceded or took place in absence of significant rearrangements in bacterial microbial community. Species B. thetaiotamicron, B. fragilis, B. dorei and other Bacteroides with reported relevance for dysbiotic medical conditions were significantly enriched in HBR, characterized with most severe inflammation symptoms, indicating a shift towards host mucin degradation and proinflammatory immune crosstalk. |
Šket, Robert; Treichel, Nicole; Debevec, Tadej; Eiken, Ola; Mekjavic, Igor; Schloter, Michael; Vital, Marius; Chandler, Jenna; Tiedje, James M; Murovec, Boštjan; Prevoršek, Zala; Stres, Blaž Hypoxia and Inactivity Related Physiological Changes (Constipation, Inflammation) Are Not Reflected at the Level of Gut Metabolites and Butyrate Producing Microbial Community: The PlanHab Study Journal Article In: Frontiers in Physiology, vol. 8, pp. 250, 2017, ISSN: 1664-042X. @article{10.3389/fphys.2017.00250,
title = {Hypoxia and Inactivity Related Physiological Changes (Constipation, Inflammation) Are Not Reflected at the Level of Gut Metabolites and Butyrate Producing Microbial Community: The PlanHab Study},
author = {Robert Šket and Nicole Treichel and Tadej Debevec and Ola Eiken and Igor Mekjavic and Michael Schloter and Marius Vital and Jenna Chandler and James M Tiedje and Boštjan Murovec and Zala Prevoršek and Blaž Stres},
url = {https://www.frontiersin.org/article/10.3389/fphys.2017.00250},
doi = {10.3389/fphys.2017.00250},
issn = {1664-042X},
year = {2017},
date = {2017-01-01},
journal = {Frontiers in Physiology},
volume = {8},
pages = {250},
abstract = {We explored the assembly of intestinal microbiota in healthy male participants during the run-in (5 day) and experimental phases (21-day normoxic bed rest (NBR), hypoxic bedrest (HBR) and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, balanced fluid and dietary intakes, controlled circadian rhythm, microbial ambiental burden and 24/7 medical surveillance. The fraction of inspired O2 (FiO2) and partial pressure of inspired O2 (PiO2) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4000 m simulated altitude), respectively. A number of parameters linked to intestinal transit spanning Bristol Stool Scale, defecation rates, zonulin, α1-antitrypsin, eosinophil derived neurotoxin, bile acids, reducing sugars, short chain fatty acids, total soluble organic carbon, water content, diet composition and food intake were measured (167 variables). The abundance, structure and diversity of butyrate producing microbial community were assessed using the two primary bacterial butyrate synthesis pathways, butyryl-CoA: acetate CoA-transferase (but) and butyrate kinase (buk) genes. Inactivity negatively affected fecal consistency and in combination with hypoxia aggravated the state of gut inflammation (p < 0.05). In contrast, gut permeability, various metabolic markers, the structure, diversity and abundance of butyrate producing microbial community were not significantly affected. Rearrangements in the butyrate producing microbial community structure were explained by experimental setup (13.4 %), experimentally structured metabolites (12.8 %) and gut metabolite-immunological markers (11.9 %), with 61.9% remaining unexplained. Many of the measured parameters were found to be correlated and were hence omitted from further analyses. The observed progressive increase in two immunological intestinal markers suggested that the transition from healthy physiological state towards the developed symptoms of low magnitude obesity-related syndromes was primarily driven by the onset of inactivity (lack of exercise in NBR) that were exacerbated by systemic hypoxia (HBR) and significantly alleviated by exercise, despite hypoxia (HAmb). Butyrate producing community in colon exhibited apparent resilience towards short-term modifications in host exercise or hypoxia. Progressive constipation (decreased intestinal motility) and increased local inflammation marker suggest that changes in microbial colonization and metabolism were taking place at the location of small intestine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We explored the assembly of intestinal microbiota in healthy male participants during the run-in (5 day) and experimental phases (21-day normoxic bed rest (NBR), hypoxic bedrest (HBR) and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, balanced fluid and dietary intakes, controlled circadian rhythm, microbial ambiental burden and 24/7 medical surveillance. The fraction of inspired O2 (FiO2) and partial pressure of inspired O2 (PiO2) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4000 m simulated altitude), respectively. A number of parameters linked to intestinal transit spanning Bristol Stool Scale, defecation rates, zonulin, α1-antitrypsin, eosinophil derived neurotoxin, bile acids, reducing sugars, short chain fatty acids, total soluble organic carbon, water content, diet composition and food intake were measured (167 variables). The abundance, structure and diversity of butyrate producing microbial community were assessed using the two primary bacterial butyrate synthesis pathways, butyryl-CoA: acetate CoA-transferase (but) and butyrate kinase (buk) genes. Inactivity negatively affected fecal consistency and in combination with hypoxia aggravated the state of gut inflammation (p < 0.05). In contrast, gut permeability, various metabolic markers, the structure, diversity and abundance of butyrate producing microbial community were not significantly affected. Rearrangements in the butyrate producing microbial community structure were explained by experimental setup (13.4 %), experimentally structured metabolites (12.8 %) and gut metabolite-immunological markers (11.9 %), with 61.9% remaining unexplained. Many of the measured parameters were found to be correlated and were hence omitted from further analyses. The observed progressive increase in two immunological intestinal markers suggested that the transition from healthy physiological state towards the developed symptoms of low magnitude obesity-related syndromes was primarily driven by the onset of inactivity (lack of exercise in NBR) that were exacerbated by systemic hypoxia (HBR) and significantly alleviated by exercise, despite hypoxia (HAmb). Butyrate producing community in colon exhibited apparent resilience towards short-term modifications in host exercise or hypoxia. Progressive constipation (decreased intestinal motility) and increased local inflammation marker suggest that changes in microbial colonization and metabolism were taking place at the location of small intestine. |
2016
|
Kravanja, Jaka; Žganec, Mario; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models Journal Article In: Sensors, vol. 16, no. 10, pp. 1740, 2016. @article{kravanja2016robust,
title = {Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models},
author = {Jaka Kravanja and Mario Žganec and Jerneja Žganec-Gros and Simon Dobrišek and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/robustdepthimageacquisitionusingmodulatedpatternprojectionandprobabilisticgraphicalmodels-2/},
doi = {10.3390/s16101740},
year = {2016},
date = {2016-10-20},
urldate = {2016-10-20},
journal = {Sensors},
volume = {16},
number = {10},
pages = {1740},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors.},
keywords = {3d imaging, 3d sensor, depth imaging, depth sensor, graphical models, modulated pattern projection, outdoor deployment, robust operation, Sensors, structured light},
pubstate = {published},
tppubtype = {article}
}
Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors. |
Scheirer, Walter; Flynn, Patrick; Ding, Changxing; Guo, Guodong; Štruc, Vitomir; Jazaery, Mohamad Al; Dobrišek, Simon; Grm, Klemen; Tao, Dacheng; Zhu, Yu; Brogan, Joel; Banerjee, Sandipan; Bharati, Aparna; Webster, Brandon Richard Report on the BTAS 2016 Video Person Recognition Evaluation Proceedings Article In: Proceedings of the IEEE International Conference on Biometrics: Theory, Applications ans Systems (BTAS), IEEE, 2016. @inproceedings{BTAS2016,
title = {Report on the BTAS 2016 Video Person Recognition Evaluation},
author = {Walter Scheirer and Patrick Flynn and Changxing Ding and Guodong Guo and Vitomir Štruc and Mohamad Al Jazaery and Simon Dobrišek and Klemen Grm and Dacheng Tao and Yu Zhu and Joel Brogan and Sandipan Banerjee and Aparna Bharati and Brandon Richard Webster},
year = {2016},
date = {2016-10-05},
booktitle = {Proceedings of the IEEE International Conference on Biometrics: Theory, Applications ans Systems (BTAS)},
publisher = {IEEE},
abstract = {This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Pointand- Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0:98 at a false accept rate of 0:01 — a remarkable advancement in performance from the competition held at FG 2015.},
keywords = {biometrics, competition, face recognition, group evaluation, PaSC, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Pointand- Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0:98 at a false accept rate of 0:01 — a remarkable advancement in performance from the competition held at FG 2015. |
Križaj, Janez; Dobrišek, Simon; Mihelič, France; Štruc, Vitomir Facial Landmark Localization from 3D Images Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), Portorož, Slovenia, 2016. @inproceedings{ERK2016Janez,
title = {Facial Landmark Localization from 3D Images},
author = {Janez Križaj and Simon Dobrišek and France Mihelič and Vitomir Štruc},
year = {2016},
date = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
address = {Portorož, Slovenia},
abstract = {A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework which trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus database for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations.},
keywords = {3D face data, 3d landmarking, Bosphorus, face alignment, face image processing, facial landmarking, SDM, supervised descent framework},
pubstate = {published},
tppubtype = {inproceedings}
}
A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework which trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus database for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations. |
Fabijan, Sebastjan; Štruc, Vitomir Vpliv registracije obraznih področij na učinkovitost samodejnega razpoznavanja obrazov: študija z OpenBR Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), 2016. @inproceedings{ERK2016_Seba,
title = {Vpliv registracije obraznih področij na učinkovitost samodejnega razpoznavanja obrazov: študija z OpenBR},
author = {Sebastjan Fabijan and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/vplivregistracijeobraznihpodrocijnaucinkovitostsamodejnegarazpoznavanjaobrazovstudijazopenbr/},
year = {2016},
date = {2016-09-20},
urldate = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
abstract = {Razpoznavanje obrazov je v zadnjih letih postalo eno najuspešnejših področij samodejne, računalniško podprte analize slik, ki se lahko pohvali z različnimi primeri upor-abe v praksi. Enega ključnih korakav za uspešno razpoznavanje predstavlja poravnava obrazov na slikah. S poravnavo poskušamo zagotoviti neodvisnost razpozn-av-an-ja od sprememb zornih kotov pri zajemu slike, ki v slikovne podatke vnašajo visoko stopnjo variabilnosti. V prispevku predstavimo tri postopke poravnavanja obrazov (iz literature) in proučimo njihov vpliv na uspešnost razpoznavanja s postopki, udejanjenimi v odprtokodnem programskem ogrodju Open Source Biometric Recognition (OpenBR). Vse poizkuse izvedemo na podatkovni zbirki Labeled Faces in the Wild (LFW).},
keywords = {4SF, biometrics, face alignment, face recognition, LFW, OpenBR, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Razpoznavanje obrazov je v zadnjih letih postalo eno najuspešnejših področij samodejne, računalniško podprte analize slik, ki se lahko pohvali z različnimi primeri upor-abe v praksi. Enega ključnih korakav za uspešno razpoznavanje predstavlja poravnava obrazov na slikah. S poravnavo poskušamo zagotoviti neodvisnost razpozn-av-an-ja od sprememb zornih kotov pri zajemu slike, ki v slikovne podatke vnašajo visoko stopnjo variabilnosti. V prispevku predstavimo tri postopke poravnavanja obrazov (iz literature) in proučimo njihov vpliv na uspešnost razpoznavanja s postopki, udejanjenimi v odprtokodnem programskem ogrodju Open Source Biometric Recognition (OpenBR). Vse poizkuse izvedemo na podatkovni zbirki Labeled Faces in the Wild (LFW). |
Stržinar, Žiga; Grm, Klemen; Štruc, Vitomir Učenje podobnosti v globokih nevronskih omrežjih za razpoznavanje obrazov Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), Portorož, Slovenia, 2016. @inproceedings{ERK2016_sebastjan,
title = {Učenje podobnosti v globokih nevronskih omrežjih za razpoznavanje obrazov},
author = {Žiga Stržinar and Klemen Grm and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/ucenjepodobnostivglobokihnevronskihomrezjihzarazpoznavanjeobrazov/},
year = {2016},
date = {2016-09-20},
urldate = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
address = {Portorož, Slovenia},
abstract = {Učenje podobnosti med pari vhodnih slik predstavlja enega najpopularnejših pristopov k razpoznavanju na področju globokega učenja. Pri tem pristopu globoko nevronsko omrežje na vhodu sprejme par slik (obrazov) in na izhodu vrne mero podobnosti med vhodnima slikama, ki jo je moč uporabiti za razpoznavanje. Izračun podobnosti je pri tem lahko v celoti udejanjen z globokim omrežjem, lahko pa se omrežje uporabi zgolj za izračun predstavitve vhodnega para slik, preslikava iz izračunane predstavitve v mero podobnosti pa se izvede z drugim, potencialno primernejšim modelom. V tem prispevku preizkusimo 5 različnih modelov za izvedbo preslikave med izračunano predstavitvijo in mero podobnosti, pri čemer za poizkuse uporabimo lastno nevronsko omrežje. Rezultati naših eksperimentov na problemu razpoznavanja obrazov kažejo na pomembnost izbire primernega modela, saj so razlike med uspešnostjo razpoznavanje od modela do modela precejšnje.},
keywords = {biometrics, CNN, deep learning, difference space, face verification, LFW, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Učenje podobnosti med pari vhodnih slik predstavlja enega najpopularnejših pristopov k razpoznavanju na področju globokega učenja. Pri tem pristopu globoko nevronsko omrežje na vhodu sprejme par slik (obrazov) in na izhodu vrne mero podobnosti med vhodnima slikama, ki jo je moč uporabiti za razpoznavanje. Izračun podobnosti je pri tem lahko v celoti udejanjen z globokim omrežjem, lahko pa se omrežje uporabi zgolj za izračun predstavitve vhodnega para slik, preslikava iz izračunane predstavitve v mero podobnosti pa se izvede z drugim, potencialno primernejšim modelom. V tem prispevku preizkusimo 5 različnih modelov za izvedbo preslikave med izračunano predstavitvijo in mero podobnosti, pri čemer za poizkuse uporabimo lastno nevronsko omrežje. Rezultati naših eksperimentov na problemu razpoznavanja obrazov kažejo na pomembnost izbire primernega modela, saj so razlike med uspešnostjo razpoznavanje od modela do modela precejšnje. |
Dobrišek, Simon; Čefarin, David; Štruc, Vitomir; Mihelič, France Assessment of the Google Speech Application Programming Interface for Automatic Slovenian Speech Recognition Proceedings Article In: Jezikovne Tehnologije in Digitalna Humanistika, 2016. @inproceedings{SJDT,
title = {Assessment of the Google Speech Application Programming Interface for Automatic Slovenian Speech Recognition},
author = {Simon Dobrišek and David Čefarin and Vitomir Štruc and France Mihelič},
url = {https://lmi.fe.uni-lj.si/en/assessmentofthegooglespeechapplicationprogramminginterfaceforautomaticslovenianspeechrecognition/},
year = {2016},
date = {2016-09-20},
urldate = {2016-09-20},
booktitle = {Jezikovne Tehnologije in Digitalna Humanistika},
abstract = {Automatic speech recognizers are slowly maturing into technologies that enable humans to communicate more naturally and effectively with a variety of smart devices and information-communication systems. Large global companies such as Google, Microsoft, Apple, IBM and Baidu compete in developing the most reliable speech recognizers, supporting as many of the main world languages as possible. Due to the relatively small number of speakers, the support for the Slovenian spoken language is lagging behind, and among the major global companies only Google has recently supported our spoken language. The paper presents the results of our independent assessment of the Google speech-application programming interface for automatic Slovenian speech recognition. For the experiments, we used speech databases that are otherwise used for the development and assessment of Slovenian speech recognizers.},
keywords = {Google, performance evaluation, speech API, speech technologies},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic speech recognizers are slowly maturing into technologies that enable humans to communicate more naturally and effectively with a variety of smart devices and information-communication systems. Large global companies such as Google, Microsoft, Apple, IBM and Baidu compete in developing the most reliable speech recognizers, supporting as many of the main world languages as possible. Due to the relatively small number of speakers, the support for the Slovenian spoken language is lagging behind, and among the major global companies only Google has recently supported our spoken language. The paper presents the results of our independent assessment of the Google speech-application programming interface for automatic Slovenian speech recognition. For the experiments, we used speech databases that are otherwise used for the development and assessment of Slovenian speech recognizers. |
Ribič, Metod; Emeršič, Žiga; Štruc, Vitomir; Peer, Peter Influence of alignment on ear recognition : case study on AWE Dataset Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), pp. 131-134, Portorož, Slovenia, 2016. @inproceedings{RibicERK2016,
title = {Influence of alignment on ear recognition : case study on AWE Dataset},
author = {Metod Ribič and Žiga Emeršič and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/en/influenceofalignmentonearrecognitioncasestudyonawedataset/},
year = {2016},
date = {2016-09-20},
urldate = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
pages = {131-134},
address = {Portorož, Slovenia},
abstract = {Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. Recently, we presented Annotated Web Ears dataset that opens new challenges in ear recognition. In this paper we test the influence of alignment on recognition performance and prove that even with the alignment the database is still very challenging, even-though the recognition rate is improved due to alignment. We also prove that more sophisticated alignment methods are needed to address the AWE dataset efficiently},
keywords = {AWE, AWE dataset, biometrics, ear alignment, ear recognition, image alignment, Ransac, SIFT},
pubstate = {published},
tppubtype = {inproceedings}
}
Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. Recently, we presented Annotated Web Ears dataset that opens new challenges in ear recognition. In this paper we test the influence of alignment on recognition performance and prove that even with the alignment the database is still very challenging, even-though the recognition rate is improved due to alignment. We also prove that more sophisticated alignment methods are needed to address the AWE dataset efficiently |
Dobrišek, Simon; Čefarin, David; Štruc, Vitomir; Mihelič, France Preizkus Googlovega govornega programskega vmesnika pri samodejnem razpoznavanju govorjene slovenščine Proceedings Article In: Jezikovne tehnologije in digitalna humanistika, pp. 47-51, 2016. @inproceedings{dobrivsekpreizkus,
title = {Preizkus Googlovega govornega programskega vmesnika pri samodejnem razpoznavanju govorjene slovenščine},
author = {Simon Dobrišek and David Čefarin and Vitomir Štruc and France Mihelič},
url = {http://www.sdjt.si/wp/wp-content/uploads/2016/09/JTDH-2016_Dobrisek-et-al_Preizkus-Googlovega-govornega-programskega-vmesnika.pdf},
year = {2016},
date = {2016-09-01},
booktitle = {Jezikovne tehnologije in digitalna humanistika},
pages = {47-51},
abstract = {Automatic speech recognizers are slowly maturing into technologies that enable humans to communicate more naturally and effectively with a variety of smart devices and information-communication systems. Large global companies such as Google, Microsoft, Apple, IBM and Baidu compete in developing the most reliable speech recognizers, supporting as many of the main world languages as possible. Due to the relatively small number of speakers, the support for the Slovenian spoken language is lagging behind, and among the major global companies only Google has recently supported our spoken language. The paper presents the results of our independent assessment of the Google speech-application programming interface for automatic Slovenian speech recognition. For the experiments, we used speech databases that are otherwise used for the development and assessment of Slovenian speech recognizers.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic speech recognizers are slowly maturing into technologies that enable humans to communicate more naturally and effectively with a variety of smart devices and information-communication systems. Large global companies such as Google, Microsoft, Apple, IBM and Baidu compete in developing the most reliable speech recognizers, supporting as many of the main world languages as possible. Due to the relatively small number of speakers, the support for the Slovenian spoken language is lagging behind, and among the major global companies only Google has recently supported our spoken language. The paper presents the results of our independent assessment of the Google speech-application programming interface for automatic Slovenian speech recognition. For the experiments, we used speech databases that are otherwise used for the development and assessment of Slovenian speech recognizers.
|
Kravanja, Jaka; Žganec, Mario; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir Exploiting Spatio-Temporal Information for Light-Plane Labeling in Depth-Image Sensors Using Probabilistic Graphical Models Journal Article In: Informatica, vol. 27, no. 1, pp. 67–84, 2016. @article{kravanja2016exploiting,
title = {Exploiting Spatio-Temporal Information for Light-Plane Labeling in Depth-Image Sensors Using Probabilistic Graphical Models},
author = {Jaka Kravanja and Mario Žganec and Jerneja Žganec-Gros and Simon Dobrišek and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/exploitingspatio-temporalinformationforlight-planelabelingindepth-imagesensorsusingprobabilisticgraphicalmodels/},
year = {2016},
date = {2016-03-30},
urldate = {2016-03-30},
journal = {Informatica},
volume = {27},
number = {1},
pages = {67--84},
publisher = {Vilnius University Institute of Mathematics and Informatics},
abstract = {This paper proposes a novel approach to light plane labeling in depth-image sensors relying on “uncoded” structured light. The proposed approach adopts probabilistic graphical models (PGMs) to solve the correspondence problem between the projected and the detected light patterns. The procedure for solving the correspondence problem is designed to take the spatial relations between the parts of the projected pattern and prior knowledge about the structure of the pattern into account, but it also exploits temporal information to achieve reliable light-plane labeling. The procedure is assessed on a database of light patterns detected with a specially developed imaging sensor that, unlike most existing solutions on the market, was shown to work reliably in outdoor environments as well as in the presence of other identical (active) sensors directed at the same scene. The results of our experiments show that the proposed approach is able to reliably solve the correspondence problem and assign light-plane labels to the detected pattern with a high accuracy, even when large spatial discontinuities are present in the observed scene.},
keywords = {3d imaging, correspondance, depth imaging, depth sensing, depth sensor, graphical models, sensor, structured light},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a novel approach to light plane labeling in depth-image sensors relying on “uncoded” structured light. The proposed approach adopts probabilistic graphical models (PGMs) to solve the correspondence problem between the projected and the detected light patterns. The procedure for solving the correspondence problem is designed to take the spatial relations between the parts of the projected pattern and prior knowledge about the structure of the pattern into account, but it also exploits temporal information to achieve reliable light-plane labeling. The procedure is assessed on a database of light patterns detected with a specially developed imaging sensor that, unlike most existing solutions on the market, was shown to work reliably in outdoor environments as well as in the presence of other identical (active) sensors directed at the same scene. The results of our experiments show that the proposed approach is able to reliably solve the correspondence problem and assign light-plane labels to the detected pattern with a high accuracy, even when large spatial discontinuities are present in the observed scene. |
Grm, Klemen; Dobrišek, Simon; Štruc, Vitomir Deep pair-wise similarity learning for face recognition Proceedings Article In: 4th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, IEEE 2016. @inproceedings{grm2016deep,
title = {Deep pair-wise similarity learning for face recognition},
author = {Klemen Grm and Simon Dobrišek and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/deeppair-wisesimilaritylearningforfacerecognition/},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {4th International Workshop on Biometrics and Forensics (IWBF)},
pages = {1--6},
organization = {IEEE},
abstract = {Recent advances in deep learning made it possible to build deep hierarchical models capable of delivering state-of-the-art performance in various vision tasks, such as object recognition, detection or tracking. For recognition tasks the most common approach when using deep models is to learn object representations (or features) directly from raw image-input and then feed the learned features to a suitable classifier. Deep models used in this pipeline are typically heavily parameterized and require enormous amounts of training data to deliver competitive recognition performance. Despite the use of data augmentation techniques, many application domains, predefined experimental protocols or specifics of the recognition problem limit the amount of available training data and make training an effective deep hierarchical model a difficult task. In this paper, we present a novel, deep pair-wise similarity learning (DPSL) strategy for deep models, developed specifically to overcome the problem of insufficient training data, and demonstrate its usage on the task of face recognition. Unlike existing (deep) learning strategies, DPSL operates on image-pairs and tries to learn pair-wise image similarities that can be used for recognition purposes directly instead of feature representations that need to be fed to appropriate classification techniques, as with traditional deep learning pipelines. Since our DPSL strategy assumes an image pair as the input to the learning procedure, the amount of training data available to train deep models is quadratic in the number of available training images, which is of paramount importance for models with a large number of parameters. We demonstrate the efficacy of the proposed learning strategy by developing a deep model for pose-invariant face recognition, called Pose-Invariant Similarity Index (PISI), and presenting comparative experimental results on the FERET an IJB-A datasets.},
keywords = {CNN, deep learning, face recognition, IJB-A, IWBF, performance evaluation, similarity learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advances in deep learning made it possible to build deep hierarchical models capable of delivering state-of-the-art performance in various vision tasks, such as object recognition, detection or tracking. For recognition tasks the most common approach when using deep models is to learn object representations (or features) directly from raw image-input and then feed the learned features to a suitable classifier. Deep models used in this pipeline are typically heavily parameterized and require enormous amounts of training data to deliver competitive recognition performance. Despite the use of data augmentation techniques, many application domains, predefined experimental protocols or specifics of the recognition problem limit the amount of available training data and make training an effective deep hierarchical model a difficult task. In this paper, we present a novel, deep pair-wise similarity learning (DPSL) strategy for deep models, developed specifically to overcome the problem of insufficient training data, and demonstrate its usage on the task of face recognition. Unlike existing (deep) learning strategies, DPSL operates on image-pairs and tries to learn pair-wise image similarities that can be used for recognition purposes directly instead of feature representations that need to be fed to appropriate classification techniques, as with traditional deep learning pipelines. Since our DPSL strategy assumes an image pair as the input to the learning procedure, the amount of training data available to train deep models is quadratic in the number of available training images, which is of paramount importance for models with a large number of parameters. We demonstrate the efficacy of the proposed learning strategy by developing a deep model for pose-invariant face recognition, called Pose-Invariant Similarity Index (PISI), and presenting comparative experimental results on the FERET an IJB-A datasets. |
Golob, Žiga; Gros, Jerneja Žganec; Štruc, Vitomir; Mihelič, France; Dobrišek, Simon A Composition Algorithm of Compact Finite-State Super Transducers for Grapheme-to-Phoneme Conversion Proceedings Article In: International Conference on Text, Speech, and Dialogue, pp. 375–382, Springer 2016. @inproceedings{golob2016composition,
title = {A Composition Algorithm of Compact Finite-State Super Transducers for Grapheme-to-Phoneme Conversion},
author = {Žiga Golob and Jerneja Žganec Gros and Vitomir Štruc and France Mihelič and Simon Dobrišek},
year = {2016},
date = {2016-01-01},
booktitle = {International Conference on Text, Speech, and Dialogue},
pages = {375--382},
organization = {Springer},
abstract = {Minimal deterministic finite-state transducers (MDFSTs) are powerful models that can be used to represent pronunciation dictionaries in a compact form. Intuitively, we would assume that by increasing the size of the dictionary, the size of the MDFSTs would increase as well. However, as we show in the paper, this intuition does not hold for highly inflected languages. With such languages the size of the MDFSTs begins to decrease once the number of words in the represented dictionary reaches a certain threshold. Motivated by this observation, we have developed a new type of FST, called a finite-state super transducer (FSST), and show experimentally that the FSST is capable of representing pronunciation dictionaries with fewer states and transitions than MDFSTs. Furthermore, we show that (unlike MDFSTs) our FSSTs can also accept words that are not part of the represented dictionary. The phonetic transcriptions of these out-of-dictionary words may not always be correct, but the observed error rates are comparable to the error rates of the traditional methods for grapheme-to-phoneme conversion.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Minimal deterministic finite-state transducers (MDFSTs) are powerful models that can be used to represent pronunciation dictionaries in a compact form. Intuitively, we would assume that by increasing the size of the dictionary, the size of the MDFSTs would increase as well. However, as we show in the paper, this intuition does not hold for highly inflected languages. With such languages the size of the MDFSTs begins to decrease once the number of words in the represented dictionary reaches a certain threshold. Motivated by this observation, we have developed a new type of FST, called a finite-state super transducer (FSST), and show experimentally that the FSST is capable of representing pronunciation dictionaries with fewer states and transitions than MDFSTs. Furthermore, we show that (unlike MDFSTs) our FSSTs can also accept words that are not part of the represented dictionary. The phonetic transcriptions of these out-of-dictionary words may not always be correct, but the observed error rates are comparable to the error rates of the traditional methods for grapheme-to-phoneme conversion. |
2015
|
Grm, Klemen; Dobrišek, Simon; Štruc, Vitomir The pose-invariant similarity index for face recognition Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), Portorož, Slovenia, 2015. @inproceedings{ERK2015Klemen,
title = {The pose-invariant similarity index for face recognition},
author = {Klemen Grm and Simon Dobrišek and Vitomir Štruc},
year = {2015},
date = {2015-04-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
address = {Portorož, Slovenia},
keywords = {biometrics, CNN, deep learning, deep models, face verification, similarity learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Štruc, Vitomir; Križaj, Janez; Dobrišek, Simon Modest face recognition Proceedings Article In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, IEEE, 2015. @inproceedings{struc2015modest,
title = {Modest face recognition},
author = {Vitomir Štruc and Janez Križaj and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/modestfacerecognition/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},
pages = {1--6},
publisher = {IEEE},
abstract = {The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework.},
keywords = {biometrics, face verification, Gabor features, image descriptors, LBP, multi modality, PaSC, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework. |
Beveridge, Ross; Zhang, Hao; Draper, Bruce A; Flynn, Patrick J; Feng, Zhenhua; Huber, Patrik; Kittler, Josef; Huang, Zhiwu; Li, Shaoxin; Li, Yan; Štruc, Vitomir; Križaj, Janez; others, Report on the FG 2015 video person recognition evaluation Proceedings Article In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG), pp. 1–8, IEEE 2015. @inproceedings{beveridge2015report,
title = {Report on the FG 2015 video person recognition evaluation},
author = {Ross Beveridge and Hao Zhang and Bruce A Draper and Patrick J Flynn and Zhenhua Feng and Patrik Huber and Josef Kittler and Zhiwu Huang and Shaoxin Li and Yan Li and Vitomir Štruc and Janez Križaj and others},
url = {https://lmi.fe.uni-lj.si/en/reportonthefg2015videopersonrecognitionevaluation/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG)},
volume = {1},
pages = {1--8},
organization = {IEEE},
abstract = {This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing.},
keywords = {biometrics, competition, face verification, FG, group evaluation, PaSC, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing. |
Justin, Tadej; Štruc, Vitomir; Dobrišek, Simon; Vesnicer, Boštjan; Ipšić, Ivo; Mihelič, France Speaker de-identification using diphone recognition and speech synthesis Proceedings Article In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): DeID 2015, pp. 1–7, IEEE 2015. @inproceedings{justin2015speaker,
title = {Speaker de-identification using diphone recognition and speech synthesis},
author = {Tadej Justin and Vitomir Štruc and Simon Dobrišek and Boštjan Vesnicer and Ivo Ipšić and France Mihelič},
url = {https://lmi.fe.uni-lj.si/en/speakerde-identificationusingdiphonerecognitionandspeechsynthesis/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): DeID 2015},
volume = {4},
pages = {1--7},
organization = {IEEE},
abstract = {The paper addresses the problem of speaker (or voice) de-identification by presenting a novel approach for concealing the identity of speakers in their speech. The proposed technique first recognizes the input speech with a diphone recognition system and then transforms the obtained phonetic transcription into the speech of another speaker with a speech synthesis system. Due to the fact that a Diphone RecOgnition step and a sPeech SYnthesis step are used during the deidentification, we refer to the developed technique as DROPSY. With this approach the acoustical models of the recognition and synthesis modules are completely independent from each other, which ensures the highest level of input speaker deidentification. The proposed DROPSY-based de-identification approach is language dependent, text independent and capable of running in real-time due to the relatively simple computing methods used. When designing speaker de-identification technology two requirements are typically imposed on the deidentification techniques: i) it should not be possible to establish the identity of the speakers based on the de-identified speech, and ii) the processed speech should still sound natural and be intelligible. This paper, therefore, implements the proposed DROPSY-based approach with two different speech synthesis techniques (i.e, with the HMM-based and the diphone TDPSOLA- based technique). The obtained de-identified speech is evaluated for intelligibility and evaluated in speaker verification experiments with a state-of-the-art (i-vector/PLDA) speaker recognition system. The comparison of both speech synthesis modules integrated in the proposed method reveals that both can efficiently de-identify the input speakers while still producing intelligible speech.},
keywords = {DEID, FG, speech deidentification, speech recognition, speech synthesis, speech technologies},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper addresses the problem of speaker (or voice) de-identification by presenting a novel approach for concealing the identity of speakers in their speech. The proposed technique first recognizes the input speech with a diphone recognition system and then transforms the obtained phonetic transcription into the speech of another speaker with a speech synthesis system. Due to the fact that a Diphone RecOgnition step and a sPeech SYnthesis step are used during the deidentification, we refer to the developed technique as DROPSY. With this approach the acoustical models of the recognition and synthesis modules are completely independent from each other, which ensures the highest level of input speaker deidentification. The proposed DROPSY-based de-identification approach is language dependent, text independent and capable of running in real-time due to the relatively simple computing methods used. When designing speaker de-identification technology two requirements are typically imposed on the deidentification techniques: i) it should not be possible to establish the identity of the speakers based on the de-identified speech, and ii) the processed speech should still sound natural and be intelligible. This paper, therefore, implements the proposed DROPSY-based approach with two different speech synthesis techniques (i.e, with the HMM-based and the diphone TDPSOLA- based technique). The obtained de-identified speech is evaluated for intelligibility and evaluated in speaker verification experiments with a state-of-the-art (i-vector/PLDA) speaker recognition system. The comparison of both speech synthesis modules integrated in the proposed method reveals that both can efficiently de-identify the input speakers while still producing intelligible speech. |
Dobrišek, Simon; Štruc, Vitomir; Križaj, Janez; Mihelič, France Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier Proceedings Article In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015, pp. 1–6, IEEE 2015. @inproceedings{dobrivsek2015face,
title = {Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier},
author = {Simon Dobrišek and Vitomir Štruc and Janez Križaj and France Mihelič},
url = {https://lmi.fe.uni-lj.si/en/facerecognitioninthewildwiththeprobabilisticgabor-fisherclassifier/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015},
volume = {2},
pages = {1--6},
organization = {IEEE},
abstract = {The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing.},
keywords = {biometrics, BWild, FG, Gabor features, PaSC, plda, probabilistic Gabor Fisher classifier, probabilistic linear discriminant analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing. |
Justin, Tadej; Štruc, Vitomir; Žibert, Janez; Mihelič, France Development and Evaluation of the Emotional Slovenian Speech Database-EmoLUKS Proceedings Article In: Proceedings of the International Conference on Text, Speech, and Dialogue (TSD), pp. 351–359, Springer 2015. @inproceedings{justin2015development,
title = {Development and Evaluation of the Emotional Slovenian Speech Database-EmoLUKS},
author = {Tadej Justin and Vitomir Štruc and Janez Žibert and France Mihelič},
url = {https://lmi.fe.uni-lj.si/en/developmentandevaluationoftheemotionalslovenianspeechdatabase-emoluks/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the International Conference on Text, Speech, and Dialogue (TSD)},
pages = {351--359},
organization = {Springer},
abstract = {This paper describes a speech database built from 17 Slovenian radio dramas. The dramas were obtained from the national radio-and-television station (RTV Slovenia) and were given at the universities disposal with an academic license for processing and annotating the audio material. The utterances of one male and one female speaker were transcribed, segmented and then annotated with emotional states of the speakers. The annotation of the emotional states was conducted in two stages with our own web-based application for crowd sourcing. The final (emotional) speech database consists of 1385 recordings of one male (975 recordings) and one female (410 recordings) speaker and contains labeled emotional speech with a total duration of around 1 hour and 15 minutes. The paper presents the two-stage annotation process used to label the data and demonstrates the usefulness of the employed annotation methodology. Baseline emotion recognition experiments are also presented. The reported results are presented with the un-weighted as well as weighted average recalls and precisions for 2-class and 7-class recognition experiments.},
keywords = {annotated data, dataset, dataset of emotional speech, EmoLUKS, emotional speech synthesis, speech synthesis, speech technologies, transcriptions},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a speech database built from 17 Slovenian radio dramas. The dramas were obtained from the national radio-and-television station (RTV Slovenia) and were given at the universities disposal with an academic license for processing and annotating the audio material. The utterances of one male and one female speaker were transcribed, segmented and then annotated with emotional states of the speakers. The annotation of the emotional states was conducted in two stages with our own web-based application for crowd sourcing. The final (emotional) speech database consists of 1385 recordings of one male (975 recordings) and one female (410 recordings) speaker and contains labeled emotional speech with a total duration of around 1 hour and 15 minutes. The paper presents the two-stage annotation process used to label the data and demonstrates the usefulness of the employed annotation methodology. Baseline emotion recognition experiments are also presented. The reported results are presented with the un-weighted as well as weighted average recalls and precisions for 2-class and 7-class recognition experiments. |
Camgoz, Necati Cihan; Štruc, Vitomir; Gokberk, Berk; Akarun, Lale; Kindiroglu, Ahmet Alp Facial Landmark Localization in Depth Images using Supervised Ridge Descent Proceedings Article In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn, pp. 136–141, 2015. @inproceedings{cihan2015facial,
title = {Facial Landmark Localization in Depth Images using Supervised Ridge Descent},
author = {Necati Cihan Camgoz and Vitomir Štruc and Berk Gokberk and Lale Akarun and Ahmet Alp Kindiroglu},
url = {https://lmi.fe.uni-lj.si/en/faciallandmarklocalizationindepthimagesusingsupervisedridgedescent/},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn},
pages = {136--141},
abstract = {Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably},
keywords = {3d landmarking, facial landmarking, landmark localization, landmarking, ridge regression, SDM},
pubstate = {published},
tppubtype = {inproceedings}
}
Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably |
Murovec, Boštjan Job-shop local-search move evaluation without direct consideration of the criterion’s value Journal Article In: European Journal of Operational Research, vol. 241, no. 2, pp. 320 - 329, 2015, ISSN: 0377-2217. @article{MUROVEC2015320,
title = {Job-shop local-search move evaluation without direct consideration of the criterion’s value},
author = {Boštjan Murovec},
url = {http://www.sciencedirect.com/science/article/pii/S0377221714007309},
doi = {https://doi.org/10.1016/j.ejor.2014.08.044},
issn = {0377-2217},
year = {2015},
date = {2015-01-01},
journal = {European Journal of Operational Research},
volume = {241},
number = {2},
pages = {320 - 329},
abstract = {This article focuses on the evaluation of moves for the local search of the job-shop problem with the makespan criterion. We reason that the omnipresent ranking of moves according to their resulting value of a criterion function makes the local search unnecessarily myopic. Consequently, we introduce an alternative evaluation that relies on a surrogate quantity of the move’s potential, which is related to, but not strongly coupled with, the bare criterion. The approach is confirmed by empirical tests, where the proposed evaluator delivers a new upper bound on the well-known benchmark test yn2. The line of the argumentation also shows that by sacrificing accuracy the established makespan estimators unintentionally improve on the move evaluation in comparison to the exact makespan calculation, in contrast to the belief that the reliance on estimation degrades the optimization results.},
keywords = {Job-shop, Local search, Makespan, Move evaluation, Scheduling},
pubstate = {published},
tppubtype = {article}
}
This article focuses on the evaluation of moves for the local search of the job-shop problem with the makespan criterion. We reason that the omnipresent ranking of moves according to their resulting value of a criterion function makes the local search unnecessarily myopic. Consequently, we introduce an alternative evaluation that relies on a surrogate quantity of the move’s potential, which is related to, but not strongly coupled with, the bare criterion. The approach is confirmed by empirical tests, where the proposed evaluator delivers a new upper bound on the well-known benchmark test yn2. The line of the argumentation also shows that by sacrificing accuracy the established makespan estimators unintentionally improve on the move evaluation in comparison to the exact makespan calculation, in contrast to the belief that the reliance on estimation degrades the optimization results. |
Murovec, Boštjan; Kolbl, Sabina; Stres, Blaž Methane Yield Database: Online infrastructure and bioresource for methane yield data and related metadata Journal Article In: Bioresource Technology, vol. 189, pp. 217 - 223, 2015, ISSN: 0960-8524. @article{MUROVEC2015217,
title = {Methane Yield Database: Online infrastructure and bioresource for methane yield data and related metadata},
author = {Boštjan Murovec and Sabina Kolbl and Blaž Stres},
url = {http://www.sciencedirect.com/science/article/pii/S0960852415005040},
doi = {https://doi.org/10.1016/j.biortech.2015.04.021},
issn = {0960-8524},
year = {2015},
date = {2015-01-01},
journal = {Bioresource Technology},
volume = {189},
pages = {217 - 223},
abstract = {The aim of this study was to develop and validate a community supported online infrastructure and bioresource for methane yield data and accompanying metadata collected from published literature. In total, 1164 entries described by 15,749 data points were assembled. Analysis of data collection showed little congruence in reporting of methodological approaches. The largest identifiable source of variation in reported methane yields was represented by authorship (i.e. substrate batches within particular substrate class) within which experimental scales (volumes (0.02–5l), incubation temperature (34–40°C) and % VS of substrate played an important role (p<0.0},
keywords = {Batch, Biogas, Industry, Infrastructure, Methane yield database},
pubstate = {published},
tppubtype = {article}
}
The aim of this study was to develop and validate a community supported online infrastructure and bioresource for methane yield data and accompanying metadata collected from published literature. In total, 1164 entries described by 15,749 data points were assembled. Analysis of data collection showed little congruence in reporting of methodological approaches. The largest identifiable source of variation in reported methane yields was represented by authorship (i.e. substrate batches within particular substrate class) within which experimental scales (volumes (0.02–5l), incubation temperature (34–40°C) and % VS of substrate played an important role (p<0.0 |
Henderson, Gemma; Cox, Faith; Ganesh, Siva; Jonker, Arjan; Young, Wayne; Janssen, Peter H Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range Journal Article In: Scientific reports, vol. art 14567, no. 5, pp. 1–13, 2015, ISSN: 2045-2322. @article{Henderson_Cox_Ganesh_Jonker_Young_Janssen_2015,
title = {Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range},
author = {Gemma Henderson and Faith Cox and Siva Ganesh and Arjan Jonker and Wayne Young and Peter H Janssen},
url = {http://www.nature.com/articles/srep14567},
doi = {10.1038/srep14567},
issn = {2045-2322},
year = {2015},
date = {2015-01-01},
journal = {Scientific reports},
volume = {art 14567},
number = {5},
pages = {1–13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2014
|
Peer, Peter; Emeršič, Žiga; Bule, Jernej; Žganec-Gros, Jerneja; Štruc, Vitomir Strategies for exploiting independent cloud implementations of biometric experts in multibiometric scenarios Journal Article In: Mathematical problems in engineering, vol. 2014, 2014. @article{peer2014strategies,
title = {Strategies for exploiting independent cloud implementations of biometric experts in multibiometric scenarios},
author = {Peter Peer and Žiga Emeršič and Jernej Bule and Jerneja Žganec-Gros and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/strategiesforexploitingindependentcloudimplementationsofbiometricexpertsinmultibiometricscenarios/},
doi = {http://dx.doi.org/10.1155/2014/585139},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Mathematical problems in engineering},
volume = {2014},
publisher = {Hindawi Publishing Corporation},
abstract = {Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.},
keywords = {application, biometrics, cloud computing, face recognition, fingerprint recognition, fusion},
pubstate = {published},
tppubtype = {article}
}
Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper. |
Štruc, Vitomir; Žganec-Gros, Jerneja; Vesnicer, Boštjan; Pavešić, Nikola Beyond parametric score normalisation in biometric verification systems Journal Article In: IET Biometrics, vol. 3, no. 2, pp. 62–74, 2014. @article{struc2014beyond,
title = {Beyond parametric score normalisation in biometric verification systems},
author = {Vitomir Štruc and Jerneja Žganec-Gros and Boštjan Vesnicer and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/beyondparametricscorenormalisationinbiometricverificationsystems/},
doi = {10.1049/iet-bmt.2013.0076},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {IET Biometrics},
volume = {3},
number = {2},
pages = {62--74},
publisher = {IET},
abstract = {Similarity scores represent the basis for identity inference in biometric verification systems. However, because of the so-called miss-matched conditions across enrollment and probe samples and identity-dependent factors these scores typically exhibit statistical variations that affect the verification performance of biometric systems. To mitigate these variations, scorenormalisation techniques, such as the z-norm, the t-norm or the zt-norm, are commonly adopted. In this study, the authors study the problem of score normalisation in the scope of biometric verification and introduce a new class of non-parametric normalisation techniques, which make no assumptions regarding the shape of the distribution from which the scores are drawn (as the parametric techniques do). Instead, they estimate the shape of the score distribution and use the estimate to map the initial distribution to a common (predefined) distribution. Based on the new class of normalisation techniques they also develop a hybrid normalisation scheme that combines non-parametric and parametric techniques into hybrid two-step procedures. They evaluate the performance of the non-parametric and hybrid techniques in face-verification experiments on the FRGCv2 and SCFace databases and show that the non-parametric techniques outperform their parametric counterparts and that the hybrid procedure is not only feasible, but also retains some desirable characteristics from both the non-parametric and the parametric techniques.},
keywords = {biometrics, face verification, hybrid score normalization, score normalization, t-norm, tz-norm, z-norm, zt-norm},
pubstate = {published},
tppubtype = {article}
}
Similarity scores represent the basis for identity inference in biometric verification systems. However, because of the so-called miss-matched conditions across enrollment and probe samples and identity-dependent factors these scores typically exhibit statistical variations that affect the verification performance of biometric systems. To mitigate these variations, scorenormalisation techniques, such as the z-norm, the t-norm or the zt-norm, are commonly adopted. In this study, the authors study the problem of score normalisation in the scope of biometric verification and introduce a new class of non-parametric normalisation techniques, which make no assumptions regarding the shape of the distribution from which the scores are drawn (as the parametric techniques do). Instead, they estimate the shape of the score distribution and use the estimate to map the initial distribution to a common (predefined) distribution. Based on the new class of normalisation techniques they also develop a hybrid normalisation scheme that combines non-parametric and parametric techniques into hybrid two-step procedures. They evaluate the performance of the non-parametric and hybrid techniques in face-verification experiments on the FRGCv2 and SCFace databases and show that the non-parametric techniques outperform their parametric counterparts and that the hybrid procedure is not only feasible, but also retains some desirable characteristics from both the non-parametric and the parametric techniques. |
Emeršič, Žiga; Bule, Jernej; Žganec-Gros, Jerneja; Štruc, Vitomir; Peer, Peter A case study on multi-modal biometrics in the cloud Journal Article In: Electrotechnical Review, vol. 81, no. 3, pp. 74, 2014. @article{emersic2014case,
title = {A case study on multi-modal biometrics in the cloud},
author = {Žiga Emeršič and Jernej Bule and Jerneja Žganec-Gros and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/en/acasestudyonmulti-modalbiometricsinthecloud/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Electrotechnical Review},
volume = {81},
number = {3},
pages = {74},
publisher = {Elektrotehniski Vestnik},
abstract = {Cloud computing is particularly interesting for the area of biometric recognition, where scalability, availability and accessibility are important aspects. In this paper we try to evaluate different strategies for combining existing uni-modal (cloud-based) biometric experts into a multi-biometric cloud-service. We analyze several fusion strategies from the perspective of performance gains, training complexity and resource consumption and discuss the results of our analysis. The experimental evaluation is conducted based on two biometric cloud-services developed in the scope of the Competence Centere CLASS, a face recognition service and a fingerprint recognition service, which are also briefly described in the paper. The presented results are important to researchers and developers working in the area of biometric services for the cloud looking for easy solutions for improving the quality of their services.},
keywords = {cloud, cloud computing, face recognition, face verification, fingerprint verification, fingerprints, fusion},
pubstate = {published},
tppubtype = {article}
}
Cloud computing is particularly interesting for the area of biometric recognition, where scalability, availability and accessibility are important aspects. In this paper we try to evaluate different strategies for combining existing uni-modal (cloud-based) biometric experts into a multi-biometric cloud-service. We analyze several fusion strategies from the perspective of performance gains, training complexity and resource consumption and discuss the results of our analysis. The experimental evaluation is conducted based on two biometric cloud-services developed in the scope of the Competence Centere CLASS, a face recognition service and a fingerprint recognition service, which are also briefly described in the paper. The presented results are important to researchers and developers working in the area of biometric services for the cloud looking for easy solutions for improving the quality of their services. |
Križaj, Janez; Štruc, Vitomir; Mihelič, France A Feasibility Study on the Use of Binary Keypoint Descriptors for 3D Face Recognition Proceedings Article In: Proceedings of the Mexican Conference on Pattern Recognition (MCPR), pp. 142–151, Springer 2014. @inproceedings{krivzaj2014feasibility,
title = {A Feasibility Study on the Use of Binary Keypoint Descriptors for 3D Face Recognition},
author = {Janez Križaj and Vitomir Štruc and France Mihelič},
url = {https://lmi.fe.uni-lj.si/en/afeasibilitystudyontheuseofbinarykeypointdescriptorsfor3dfacerecognition/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the Mexican Conference on Pattern Recognition (MCPR)},
pages = {142--151},
organization = {Springer},
abstract = {Despite the progress made in the area of local image descriptors in recent years, virtually no literature is available on the use of more recent descriptors for the problem of 3D face recognition, such as BRIEF, ORB, BRISK or FREAK, which are binary in nature and, therefore, tend to be faster to compute and match, while requiring signi cantly less memory for storage than, for example, SIFT or SURF. In this paper, we try to close this gap and present a feasibility study on the use of these descriptors for 3D face recognition. Descriptors are evaluated on the three challenging 3D face image datasets, namely, the FRGC, UMB and CASIA. Our experiments show the binary descriptors ensure slightly lower veri cation rates than SIFT, comparable to those of the SURF descriptor, while being an order of magnitude faster than SIFT. The results suggest that the use of binary descriptors represents a viable alternative to the established descriptors.},
keywords = {3d face recognition, binary descriptors, biometrics, BRISK, CASIA, face verification, FREAK, FRGC, MCPR, ORB, performance evaluation, SIFT, SURF},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the progress made in the area of local image descriptors in recent years, virtually no literature is available on the use of more recent descriptors for the problem of 3D face recognition, such as BRIEF, ORB, BRISK or FREAK, which are binary in nature and, therefore, tend to be faster to compute and match, while requiring signi cantly less memory for storage than, for example, SIFT or SURF. In this paper, we try to close this gap and present a feasibility study on the use of these descriptors for 3D face recognition. Descriptors are evaluated on the three challenging 3D face image datasets, namely, the FRGC, UMB and CASIA. Our experiments show the binary descriptors ensure slightly lower veri cation rates than SIFT, comparable to those of the SURF descriptor, while being an order of magnitude faster than SIFT. The results suggest that the use of binary descriptors represents a viable alternative to the established descriptors. |
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon; Marčetić, Darijan; Ribarić, Slobodan SIFT vs. FREAK: Assessing the usefulness of two keypoint descriptors for 3D face verification Proceedings Article In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1336–1341, Mipro Opatija, Croatia, 2014. @inproceedings{krivzaj2014sift,
title = {SIFT vs. FREAK: Assessing the usefulness of two keypoint descriptors for 3D face verification},
author = {Janez Križaj and Vitomir Štruc and Simon Dobrišek and Darijan Marčetić and Slobodan Ribarić},
url = {https://lmi.fe.uni-lj.si/en/siftvs-freakassessingtheusefulnessoftwokeypointdescriptorsfor3dfaceverification/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)},
pages = {1336--1341},
address = {Opatija, Croatia},
organization = {Mipro},
abstract = {Many techniques in the area of 3D face recognition rely on local descriptors to characterize the surface-shape information around points of interest (or keypoints) in the 3D images. Despite the fact that a lot of advancements have been made in the area of keypoint descriptors over the last years, the literature on 3D-face recognition for the most part still focuses on established descriptors, such as SIFT and SURF, and largely neglects more recent descriptors, such as the FREAK descriptor. In this paper we try to bridge this gap and assess the usefulness of the FREAK descriptor for the task for 3D face recognition. Of particular interest to us is a direct comparison of the FREAK and SIFT descriptors within a simple verification framework. To evaluate our framework with the two descriptors, we conduct 3D face recognition experiments on the challenging FRGCv2 and UMBDB databases and show that the FREAK descriptor ensures a very competitive verification performance when compared to the SIFT descriptor, but at a fraction of the computational cost. Our results indicate that the FREAK descriptor is a viable alternative to the SIFT descriptor for the problem of 3D face verification and due to its binary nature is particularly useful for real-time recognition systems and verification techniques for low-resource devices such as mobile phones, tablets and alike.},
keywords = {3d face recognition, binary descriptors, face recognition, FREAK, performance comparison, performance evaluation, SIFT},
pubstate = {published},
tppubtype = {inproceedings}
}
Many techniques in the area of 3D face recognition rely on local descriptors to characterize the surface-shape information around points of interest (or keypoints) in the 3D images. Despite the fact that a lot of advancements have been made in the area of keypoint descriptors over the last years, the literature on 3D-face recognition for the most part still focuses on established descriptors, such as SIFT and SURF, and largely neglects more recent descriptors, such as the FREAK descriptor. In this paper we try to bridge this gap and assess the usefulness of the FREAK descriptor for the task for 3D face recognition. Of particular interest to us is a direct comparison of the FREAK and SIFT descriptors within a simple verification framework. To evaluate our framework with the two descriptors, we conduct 3D face recognition experiments on the challenging FRGCv2 and UMBDB databases and show that the FREAK descriptor ensures a very competitive verification performance when compared to the SIFT descriptor, but at a fraction of the computational cost. Our results indicate that the FREAK descriptor is a viable alternative to the SIFT descriptor for the problem of 3D face verification and due to its binary nature is particularly useful for real-time recognition systems and verification techniques for low-resource devices such as mobile phones, tablets and alike. |
Marčetić, Darijan; Ribarić, Slobodan; Štruc, Vitomir; Pavešić, Nikola An experimental tattoo de-identification system for privacy protection in still images Proceedings Article In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1288–1293, Mipro IEEE, 2014. @inproceedings{marcetic2014experimental,
title = {An experimental tattoo de-identification system for privacy protection in still images},
author = {Darijan Marčetić and Slobodan Ribarić and Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/anexperimentaltattoode-identificationsystemforprivacyprotectioninstillimages/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)},
pages = {1288--1293},
publisher = {IEEE},
organization = {Mipro},
abstract = {An experimental tattoo de-identification system for privacy protection in still images is described in the paper. The system consists of the following modules: skin detection, region of interest detection, feature extraction, tattoo database, matching, tattoo detection, skin swapping, and quality evaluation. Two methods for tattoo localization are presented. The first is a simple ad-hoc method based only on skin colour. The second is based on skin colour, texture and SIFT features. The appearance of each tattoo area is de-identified in such a way that its skin colour and skin texture are similar to the surrounding skin area. Experimental results for still images in which tattoo location, distance, size, illumination, and motion blur have large variability are presented. The system is subjectively evaluated based on the results of tattoo localization, the level of privacy protection and the naturalness of the de-identified still images. The level of privacy protection is estimated based on the quality of the removal of the tattoo appearance and the concealment of its location.},
keywords = {computer vision, deidentification, MIPRO, privacy protection, tattoo deidentification},
pubstate = {published},
tppubtype = {inproceedings}
}
An experimental tattoo de-identification system for privacy protection in still images is described in the paper. The system consists of the following modules: skin detection, region of interest detection, feature extraction, tattoo database, matching, tattoo detection, skin swapping, and quality evaluation. Two methods for tattoo localization are presented. The first is a simple ad-hoc method based only on skin colour. The second is based on skin colour, texture and SIFT features. The appearance of each tattoo area is de-identified in such a way that its skin colour and skin texture are similar to the surrounding skin area. Experimental results for still images in which tattoo location, distance, size, illumination, and motion blur have large variability are presented. The system is subjectively evaluated based on the results of tattoo localization, the level of privacy protection and the naturalness of the de-identified still images. The level of privacy protection is estimated based on the quality of the removal of the tattoo appearance and the concealment of its location. |
Vesnicer, Boštjan; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir Incorporating Duration Information into I-Vector-Based Speaker-Recognition Systems Proceedings Article In: Proceedings of Odyssey: The Speaker and Language Recognition Workshop, pp. 241–248, 2014. @inproceedings{vesnicer2014incorporating,
title = {Incorporating Duration Information into I-Vector-Based Speaker-Recognition Systems},
author = {Boštjan Vesnicer and Jerneja Žganec-Gros and Simon Dobrišek and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/incorporatingdurationinformationintoi-vector-basedspeaker-recognitionsystems/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of Odyssey: The Speaker and Language Recognition Workshop},
pages = {241--248},
abstract = {Most of the existing literature on i-vector-based speaker recognition focuses on recognition problems, where i-vectors are extracted from speech recordings of sufficient length. The majority of modeling/recognition techniques therefore simply ignores the fact that the i-vectors are most likely estimated unreliably when short recordings are used for their computation. Only recently, were a number of solutions proposed in the literature to address the problem of duration variability, all treating the i-vector as a random variable whose posterior distribution can be parameterized by the posterior mean and the posterior covariance. In this setting the covariance matrix serves as a measure of uncertainty that is related to the length of the available recording. In contract to these solutions, we address the problem of duration variability through weighted statistics. We demonstrate in the paper how established feature transformation techniques regularly used in the area of speaker recognition, such as PCA or WCCN, can be modified to take duration into account. We evaluate our weighting scheme in the scope of the i-vector challenge organized as part of the Odyssey, Speaker and Language Recognition Workshop 2014 and achieve a minimal DCF of 0.280, which at the time of writing puts our approach in third place among all the participating institutions.},
keywords = {acustic features, biometrics, duration, duration modeling, i-vector, i-vector challenge, Odyssey, performance evaluation, speaker recognition, speech technologies},
pubstate = {published},
tppubtype = {inproceedings}
}
Most of the existing literature on i-vector-based speaker recognition focuses on recognition problems, where i-vectors are extracted from speech recordings of sufficient length. The majority of modeling/recognition techniques therefore simply ignores the fact that the i-vectors are most likely estimated unreliably when short recordings are used for their computation. Only recently, were a number of solutions proposed in the literature to address the problem of duration variability, all treating the i-vector as a random variable whose posterior distribution can be parameterized by the posterior mean and the posterior covariance. In this setting the covariance matrix serves as a measure of uncertainty that is related to the length of the available recording. In contract to these solutions, we address the problem of duration variability through weighted statistics. We demonstrate in the paper how established feature transformation techniques regularly used in the area of speaker recognition, such as PCA or WCCN, can be modified to take duration into account. We evaluate our weighting scheme in the scope of the i-vector challenge organized as part of the Odyssey, Speaker and Language Recognition Workshop 2014 and achieve a minimal DCF of 0.280, which at the time of writing puts our approach in third place among all the participating institutions. |
Beveridge, Ross; Zhang, Hao; Flynn, Patrick; Lee, Yooyoung; Liong, Venice Erin; Lu, Jiwen; de Angeloni, Marcus Assis; de Pereira, Tiago Freitas; Li, Haoxiang; Hua, Gang; Štruc, Vitomir; Križaj, Janez; Phillips, Jonathon The ijcb 2014 pasc video face and person recognition competition Proceedings Article In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8, IEEE 2014. @inproceedings{beveridge2014ijcb,
title = {The ijcb 2014 pasc video face and person recognition competition},
author = {Ross Beveridge and Hao Zhang and Patrick Flynn and Yooyoung Lee and Venice Erin Liong and Jiwen Lu and Marcus Assis de Angeloni and Tiago Freitas de Pereira and Haoxiang Li and Gang Hua and Vitomir Štruc and Janez Križaj and Jonathon Phillips},
url = {https://lmi.fe.uni-lj.si/en/theijcb2014pascvideofaceandpersonrecognitioncompetition/},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB)},
pages = {1--8},
organization = {IEEE},
abstract = {The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing the query video to a set of target videos. Both target and query videos are drawn from the same pool of 1401 videos. In the Still-to-video Experiment the person in a query video is to be recognized by comparing the query video to a larger target set consisting of still images. Algorithm performance is characterized by verification rate at a false accept rate of 0:01 and associated receiver operating characteristic (ROC) curves. Participants were provided eye coordinates for video frames. Results were submitted by 4 institutions: (i) Advanced Digital Science Center, Singapore; (ii) CPqD, Brasil; (iii) Stevens Institute of Technology, USA; and (iv) University of Ljubljana, Slovenia. Most competitors demonstrated video face recognition performance superior to the baseline provided with PaSC. The results represent the best performance to date on the handheld video portion of the PaSC.},
keywords = {biometrics, competition, face recognition, group evaluation, IJCB, PaSC, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing the query video to a set of target videos. Both target and query videos are drawn from the same pool of 1401 videos. In the Still-to-video Experiment the person in a query video is to be recognized by comparing the query video to a larger target set consisting of still images. Algorithm performance is characterized by verification rate at a false accept rate of 0:01 and associated receiver operating characteristic (ROC) curves. Participants were provided eye coordinates for video frames. Results were submitted by 4 institutions: (i) Advanced Digital Science Center, Singapore; (ii) CPqD, Brasil; (iii) Stevens Institute of Technology, USA; and (iv) University of Ljubljana, Slovenia. Most competitors demonstrated video face recognition performance superior to the baseline provided with PaSC. The results represent the best performance to date on the handheld video portion of the PaSC. |
2013
|
Križaj, Janez; Dobrišek, Simon; Štruc, Vitomir; Pavešić, Nikola Robust 3D face recognition using adapted statistical models Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK'13), 2013. @inproceedings{krizajrobust,
title = {Robust 3D face recognition using adapted statistical models},
author = {Janez Križaj and Simon Dobrišek and Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/robust3dfacerecognitionusingadaptedstatisticalmodels/},
year = {2013},
date = {2013-09-20},
urldate = {2013-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK'13)},
abstract = {The paper presents a novel framework to 3D face recognition that exploits region covariance matrices (RCMs), Gaussian mixture models (GMMs) and support vector machine (SVM) classifiers. The proposed framework first combines several 3D face representations at the feature level using RCM descriptors and then derives low-dimensional feature vectors from the computed descriptors with the unscented transform. By doing so, it enables computations in Euclidean space, and makes Gaussian mixture modeling feasible. Finally, a support vector classifier is used for identity inference. As demonstrated by our experimental results on the FRGCv2 and UMB databases, the proposed framework is highly robust and exhibits desirable characteristics such as an inherent mechanism for data fusion (through the RCMs), the ability to examine local as well as global structures of the face with the same descriptor, the ability to integrate domain-specific prior knowledge into the modeling procedure and consequently to handle missing or unreliable data.},
keywords = {3d face recognition, biometrics, covariance descriptor, face verification, FRGC, GMM, modeling, performance evaluation, region-covariance matrix},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents a novel framework to 3D face recognition that exploits region covariance matrices (RCMs), Gaussian mixture models (GMMs) and support vector machine (SVM) classifiers. The proposed framework first combines several 3D face representations at the feature level using RCM descriptors and then derives low-dimensional feature vectors from the computed descriptors with the unscented transform. By doing so, it enables computations in Euclidean space, and makes Gaussian mixture modeling feasible. Finally, a support vector classifier is used for identity inference. As demonstrated by our experimental results on the FRGCv2 and UMB databases, the proposed framework is highly robust and exhibits desirable characteristics such as an inherent mechanism for data fusion (through the RCMs), the ability to examine local as well as global structures of the face with the same descriptor, the ability to integrate domain-specific prior knowledge into the modeling procedure and consequently to handle missing or unreliable data. |
Štruc, Vitomir; Žganec-Gros, Jerneja; Pavešić, Nikola; Dobrišek, Simon Zlivanje informacij za zanseljivo in robustno razpoznavanje obrazov Journal Article In: Electrotechnical Review, vol. 80, no. 3, pp. 1-12, 2013. @article{EV_Struc_2013,
title = {Zlivanje informacij za zanseljivo in robustno razpoznavanje obrazov},
author = {Vitomir Štruc and Jerneja Žganec-Gros and Nikola Pavešić and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/zlivanjeinformacijzazanseljivoinrobustnorazpoznavanjeobrazov/},
year = {2013},
date = {2013-09-01},
urldate = {2013-09-01},
journal = {Electrotechnical Review},
volume = {80},
number = {3},
pages = {1-12},
abstract = {The existing face recognition technology has reached a performance level where it is possible to deploy it in various applications providing they are capable of ensuring controlled conditions for the image acquisition procedure. However, the technology still struggles with its recognition performance when deployed in uncontrolled and unconstrained conditions. In this paper, we present a novel approach to face recognition designed specifically for these challenging conditions. The proposed approach exploits information fusion to achieve robustness. In the first step, the approach crops the facial region from each input image in three different ways. It then maps each of the three crops into one of four color representations and finally extracts several feature types from each of the twelve facial representations. The described procedure results in a total of thirty facial representations that are combined at the matching score level using a fusion approach based on linear logistic regression (LLR) to arrive at a robust decision regarding the identity of the subject depicted in the input face image. The presented approach was enlisted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face-recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the approach, elaborate on the results of the competition and, most importantly, present some interesting findings made during our development work that are also of relevance to the research community working in the field of face recognition.},
keywords = {biometrics, face recognition, fusion, performance evaluation},
pubstate = {published},
tppubtype = {article}
}
The existing face recognition technology has reached a performance level where it is possible to deploy it in various applications providing they are capable of ensuring controlled conditions for the image acquisition procedure. However, the technology still struggles with its recognition performance when deployed in uncontrolled and unconstrained conditions. In this paper, we present a novel approach to face recognition designed specifically for these challenging conditions. The proposed approach exploits information fusion to achieve robustness. In the first step, the approach crops the facial region from each input image in three different ways. It then maps each of the three crops into one of four color representations and finally extracts several feature types from each of the twelve facial representations. The described procedure results in a total of thirty facial representations that are combined at the matching score level using a fusion approach based on linear logistic regression (LLR) to arrive at a robust decision regarding the identity of the subject depicted in the input face image. The presented approach was enlisted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face-recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the approach, elaborate on the results of the competition and, most importantly, present some interesting findings made during our development work that are also of relevance to the research community working in the field of face recognition. |
Štruc, Vitomir; Gros, Jeneja Žganec; Dobrišek, Simon; Pavešić, Nikola Exploiting representation plurality for robust and efficient face recognition Proceedings Article In: Proceedings of the 22nd Intenational Electrotechnical and Computer Science Conference (ERK'13), pp. 121–124, Portorož, Slovenia, 2013. @inproceedings{ERK2013_Struc,
title = {Exploiting representation plurality for robust and efficient face recognition},
author = {Vitomir Štruc and Jeneja Žganec Gros and Simon Dobrišek and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/exploitingrepresentationpluralityforrobustandefficientfacerecognition/},
year = {2013},
date = {2013-09-01},
urldate = {2013-09-01},
booktitle = {Proceedings of the 22nd Intenational Electrotechnical and Computer Science Conference (ERK'13)},
volume = {vol. B},
pages = {121--124},
address = {Portorož, Slovenia},
abstract = {The paper introduces a novel approach to face recognition that exploits plurality of representation to achieve robust face recognition. The proposed approach was submitted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the submitted approach, elaborate on the results of the competition and, most importantly, present some general findings made during our development work that are of relevance to the broader (face recognition) research community.},
keywords = {competition, erk, face recognition, face verification, group evaluation, ICB, mobile biometrics, MOBIO, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces a novel approach to face recognition that exploits plurality of representation to achieve robust face recognition. The proposed approach was submitted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the submitted approach, elaborate on the results of the competition and, most importantly, present some general findings made during our development work that are of relevance to the broader (face recognition) research community. |
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Combining 3D face representations using region covariance descriptors and statistical models Proceedings Article In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (IEEE FG), Workshop on 3D Face Biometrics, IEEE, Shanghai, China, 2013. @inproceedings{FG2013,
title = {Combining 3D face representations using region covariance descriptors and statistical models},
author = {Janez Križaj and Vitomir Štruc and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/combining3dfacerepresentationsusingregioncovariancedescriptorsandstatisticalmodels/},
year = {2013},
date = {2013-05-01},
urldate = {2013-05-01},
booktitle = {Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (IEEE FG), Workshop on 3D Face Biometrics},
publisher = {IEEE},
address = {Shanghai, China},
abstract = {The paper introduces a novel framework for 3D face recognition that capitalizes on region covariance descriptors and Gaussian mixture models. The framework presents an elegant and coherent way of combining multiple facial representations, while simultaneously examining all computed representations at various levels of locality. The framework first computes a number of region covariance matrices/descriptors from different sized regions of several image representations and then adopts the unscented transform to derive low-dimensional feature vectors from the computed descriptors. By doing so, it enables computations in the Euclidean space, and makes Gaussian mixture modeling feasible. In the last step a support vector machine classification scheme is used to make a decision regarding the identity of the modeled input 3D face image. The proposed framework exhibits several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrices), the ability to examine the facial images at different levels of locality, and the ability to integrate domain-specific prior knowledge into the modeling procedure. We assess the feasibility of the proposed framework on the Face Recognition Grand Challenge version 2 (FRGCv2) database with highly encouraging results.},
keywords = {3d face recognition, biometrics, covariance descriptors, face recognition, face verification, FG, gaussian mixture models, GMM, unscented transform},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces a novel framework for 3D face recognition that capitalizes on region covariance descriptors and Gaussian mixture models. The framework presents an elegant and coherent way of combining multiple facial representations, while simultaneously examining all computed representations at various levels of locality. The framework first computes a number of region covariance matrices/descriptors from different sized regions of several image representations and then adopts the unscented transform to derive low-dimensional feature vectors from the computed descriptors. By doing so, it enables computations in the Euclidean space, and makes Gaussian mixture modeling feasible. In the last step a support vector machine classification scheme is used to make a decision regarding the identity of the modeled input 3D face image. The proposed framework exhibits several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrices), the ability to examine the facial images at different levels of locality, and the ability to integrate domain-specific prior knowledge into the modeling procedure. We assess the feasibility of the proposed framework on the Face Recognition Grand Challenge version 2 (FRGCv2) database with highly encouraging results. |
Dobrišek, Simon; Gajšek, Rok; Mihelič, France; Pavešić, Nikola; Štruc, Vitomir Towards efficient multi-modal emotion recognition Journal Article In: International Journal of Advanced Robotic Systems, vol. 10, no. 53, 2013. @article{dobrivsek2013towards,
title = {Towards efficient multi-modal emotion recognition},
author = {Simon Dobrišek and Rok Gajšek and France Mihelič and Nikola Pavešić and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/towardsefficientmulti-modalemotionrecognition/},
doi = {10.5772/54002},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {International Journal of Advanced Robotic Systems},
volume = {10},
number = {53},
abstract = {The paper presents a multi-modal emotion recognition system exploiting audio and video (i.e., facial expression) information. The system first processes both sources of information individually to produce corresponding matching scores and then combines the computed matching scores to obtain a classification decision. For the video part of the system, a novel approach to emotion recognition, relying on image-set matching, is developed. The proposed approach avoids the need for detecting and tracking specific facial landmarks throughout the given video sequence, which represents a common source of error in video-based emotion recognition systems, and, therefore, adds robustness to the video processing chain. The audio part of the system, on the other hand, relies on utterance-specific Gaussian Mixture Models (GMMs) adapted from a Universal Background Model (UBM) via the maximum a posteriori probability (MAP) estimation. It improves upon the standard UBM-MAP procedure by exploiting gender information when building the utterance-specific GMMs, thus ensuring enhanced emotion recognition performance. Both the uni-modal parts as well as the combined system are assessed on the challenging multi-modal eNTERFACE'05 corpus with highly encouraging results. The developed system represents a feasible solution to emotion recognition that can easily be integrated into various systems, such as humanoid robots, smart surveillance systems and alike.},
keywords = {avid database, emotion recognition, facial expression recognition, multi modality, speech technologies},
pubstate = {published},
tppubtype = {article}
}
The paper presents a multi-modal emotion recognition system exploiting audio and video (i.e., facial expression) information. The system first processes both sources of information individually to produce corresponding matching scores and then combines the computed matching scores to obtain a classification decision. For the video part of the system, a novel approach to emotion recognition, relying on image-set matching, is developed. The proposed approach avoids the need for detecting and tracking specific facial landmarks throughout the given video sequence, which represents a common source of error in video-based emotion recognition systems, and, therefore, adds robustness to the video processing chain. The audio part of the system, on the other hand, relies on utterance-specific Gaussian Mixture Models (GMMs) adapted from a Universal Background Model (UBM) via the maximum a posteriori probability (MAP) estimation. It improves upon the standard UBM-MAP procedure by exploiting gender information when building the utterance-specific GMMs, thus ensuring enhanced emotion recognition performance. Both the uni-modal parts as well as the combined system are assessed on the challenging multi-modal eNTERFACE'05 corpus with highly encouraging results. The developed system represents a feasible solution to emotion recognition that can easily be integrated into various systems, such as humanoid robots, smart surveillance systems and alike. |
Peer, Peter; Bule, Jernej; Gros, Jerneja Žganec; Štruc, Vitomir Building cloud-based biometric services Journal Article In: Informatica, vol. 37, no. 2, pp. 115, 2013. @article{peer2013building,
title = {Building cloud-based biometric services},
author = {Peter Peer and Jernej Bule and Jerneja Žganec Gros and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/buildingcloud-basedbiometricservices/},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {Informatica},
volume = {37},
number = {2},
pages = {115},
publisher = {Slovenian Society Informatika/Slovensko drustvo Informatika},
abstract = {Over the next few years the amount of biometric data being at the disposal of various agencies and authentication service providers is expected to grow significantly. Such quantities of data require not only enormous amounts of storage but unprecedented processing power as well. To be able to face this future challenges more and more people are looking towards cloud computing, which can address these challenges quite effectively with its seemingly unlimited storage capacity, rapid data distribution and parallel processing capabilities. Since the available literature on how to implement cloud-based biometric services is extremely scarce, this paper capitalizes on the most important challenges encountered during the development work on biometric services, presents the most important standards and recommendations pertaining to biometric services in the cloud and ultimately, elaborates on the potential value of cloud-based biometric solutions by presenting a few existing (commercial) examples. In the final part of the paper, a case study on fingerprint recognition in the cloud and its integration into the e-learning environment Moodle is presented.},
keywords = {biometrics, cloud computing, development. SaaS, face recognition, fingerprint recognition},
pubstate = {published},
tppubtype = {article}
}
Over the next few years the amount of biometric data being at the disposal of various agencies and authentication service providers is expected to grow significantly. Such quantities of data require not only enormous amounts of storage but unprecedented processing power as well. To be able to face this future challenges more and more people are looking towards cloud computing, which can address these challenges quite effectively with its seemingly unlimited storage capacity, rapid data distribution and parallel processing capabilities. Since the available literature on how to implement cloud-based biometric services is extremely scarce, this paper capitalizes on the most important challenges encountered during the development work on biometric services, presents the most important standards and recommendations pertaining to biometric services in the cloud and ultimately, elaborates on the potential value of cloud-based biometric solutions by presenting a few existing (commercial) examples. In the final part of the paper, a case study on fingerprint recognition in the cloud and its integration into the e-learning environment Moodle is presented. |
Kenk, Vildana Sulič; Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Smart surveillance technologies in border control Journal Article In: European Journal of Law and Technology, vol. 4, no. 2, 2013. @article{kenk2013smart,
title = {Smart surveillance technologies in border control},
author = {Vildana Sulič Kenk and Janez Križaj and Vitomir Štruc and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/smartsurveillancetechnologiesinbordercontrol/},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {European Journal of Law and Technology},
volume = {4},
number = {2},
abstract = {The paper addresses the technical and legal aspects of the existing and forthcoming intelligent ('smart') surveillance technologies that are (or are considered to be) employed in the border control application area. Such technologies provide a computerized decision-making support to border control authorities, and are intended to increase the reliability and efficiency of border control measures. However, the question that arises is how effective these technologies are, as well as at what price, economically, socially, and in terms of citizens' rights. The paper provides a brief overview of smart surveillance technologies in border control applications, especially those used for controlling cross-border traffic, discusses possible proportionality issues and privacy risks raised by the increasingly widespread use of such technologies, as well as good/best practises developed in this area. In a broader context, the paper presents the result of the research carried out as part of the SMART (Scalable Measures for Automated Recognition Technologies) project.},
keywords = {border control, proportionality, smart surveillance, surveillance, surveillance technology},
pubstate = {published},
tppubtype = {article}
}
The paper addresses the technical and legal aspects of the existing and forthcoming intelligent ('smart') surveillance technologies that are (or are considered to be) employed in the border control application area. Such technologies provide a computerized decision-making support to border control authorities, and are intended to increase the reliability and efficiency of border control measures. However, the question that arises is how effective these technologies are, as well as at what price, economically, socially, and in terms of citizens' rights. The paper provides a brief overview of smart surveillance technologies in border control applications, especially those used for controlling cross-border traffic, discusses possible proportionality issues and privacy risks raised by the increasingly widespread use of such technologies, as well as good/best practises developed in this area. In a broader context, the paper presents the result of the research carried out as part of the SMART (Scalable Measures for Automated Recognition Technologies) project. |
Štruc, Vitomir; Pavešić, Nikola; Žganec-Gros, Jerneja; Vesnicer, Boštjan Patch-wise low-dimensional probabilistic linear discriminant analysis for Face Recognition Proceedings Article In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2352–2356, IEEE 2013. @inproceedings{vstruc2013patch,
title = {Patch-wise low-dimensional probabilistic linear discriminant analysis for Face Recognition},
author = {Vitomir Štruc and Nikola Pavešić and Jerneja Žganec-Gros and Boštjan Vesnicer},
url = {https://lmi.fe.uni-lj.si/en/patch-wiselow-dimensionalprobabilisticlineardiscriminantanalysisforfacerecognition/},
doi = {10.1109/ICASSP.2013.6638075},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {2352--2356},
organization = {IEEE},
abstract = {The paper introduces a novel approach to face recognition based on the recently proposed low-dimensional probabilistic linear discriminant analysis (LD-PLDA). The proposed approach is specifically designed for complex recognition tasks, where highly nonlinear face variations are typically encountered. Such data variations are commonly induced by changes in the external illumination conditions, viewpoint changes or expression variations and represent quite a challenge even for state-of-the-art techniques, such as LD-PLDA. To overcome this problem, we propose here a patch-wise form of the LDPLDA technique (i.e., PLD-PLDA), which relies on local image patches rather than the entire image to make inferences about the identity of the input images. The basic idea here is to decompose the complex face recognition problem into simpler problems, for which the linear nature of the LD-PLDA technique may be better suited. By doing so, several similarity scores are derived from one facial image, which are combined at the final stage using a simple sum-rule fusion scheme to arrive at a single score that can be employed for identity inference. We evaluate the proposed technique on experiment 4 of the Face Recognition Grand Challenge (FRGCv2) database with highly promising results.},
keywords = {biometrics, face verification, FRGC, ICASSP, patch-wise approach, plda, probabilistic linear discriminant analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces a novel approach to face recognition based on the recently proposed low-dimensional probabilistic linear discriminant analysis (LD-PLDA). The proposed approach is specifically designed for complex recognition tasks, where highly nonlinear face variations are typically encountered. Such data variations are commonly induced by changes in the external illumination conditions, viewpoint changes or expression variations and represent quite a challenge even for state-of-the-art techniques, such as LD-PLDA. To overcome this problem, we propose here a patch-wise form of the LDPLDA technique (i.e., PLD-PLDA), which relies on local image patches rather than the entire image to make inferences about the identity of the input images. The basic idea here is to decompose the complex face recognition problem into simpler problems, for which the linear nature of the LD-PLDA technique may be better suited. By doing so, several similarity scores are derived from one facial image, which are combined at the final stage using a simple sum-rule fusion scheme to arrive at a single score that can be employed for identity inference. We evaluate the proposed technique on experiment 4 of the Face Recognition Grand Challenge (FRGCv2) database with highly promising results. |
Günther, Manuel; Costa-Pazo, Artur; Ding, Changxing; Boutellaa, Elhocine; Chiachia, Giovani; Zhang, Honglei; de Angeloni, Marcus Assis; Štruc, Vitomir; Khoury, Elie; Vazquez-Fernandez, Esteban; others, The 2013 face recognition evaluation in mobile environment Proceedings Article In: Proceedings of the IAPR International Conference on Biometrics (ICB), pp. 1–7, IAPR 2013. @inproceedings{gunther20132013,
title = {The 2013 face recognition evaluation in mobile environment},
author = {Manuel Günther and Artur Costa-Pazo and Changxing Ding and Elhocine Boutellaa and Giovani Chiachia and Honglei Zhang and Marcus Assis de Angeloni and Vitomir Štruc and Elie Khoury and Esteban Vazquez-Fernandez and others},
url = {https://lmi.fe.uni-lj.si/en/the2013facerecognitionevaluationinmobileenvironment/},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {Proceedings of the IAPR International Conference on Biometrics (ICB)},
pages = {1--7},
organization = {IAPR},
abstract = {Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.},
keywords = {biometrics, competition, face recognition, face verification, group evaluation, mobile biometrics, MOBIO, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. |
Stres, Blaz; Sul, Woo Jun; Murovec, Bostjan; Tiedje, James M Recently Deglaciated High-Altitude Soils of the Himalaya: Diverse Environments, Heterogenous Bacterial Communities and Long-Range Dust Inputs from the Upper Troposphere Journal Article In: PLOS ONE, vol. 8, no. 9, pp. 1-10, 2013. @article{10.1371/journal.pone.0076440,
title = {Recently Deglaciated High-Altitude Soils of the Himalaya: Diverse Environments, Heterogenous Bacterial Communities and Long-Range Dust Inputs from the Upper Troposphere},
author = {Blaz Stres and Woo Jun Sul and Bostjan Murovec and James M Tiedje},
url = {https://doi.org/10.1371/journal.pone.0076440},
doi = {10.1371/journal.pone.0076440},
year = {2013},
date = {2013-01-01},
journal = {PLOS ONE},
volume = {8},
number = {9},
pages = {1-10},
publisher = {Public Library of Science},
abstract = {Background The Himalaya with its altitude and geographical position forms a barrier to atmospheric transport, which produces much aqueous-particle monsoon precipitation and makes it the largest continuous ice-covered area outside polar regions. There is a paucity of data on high-altitude microbial communities, their native environments and responses to environmental-spatial variables relative to seasonal and deglaciation events. Methodology/Principal Findings Soils were sampled along altitude transects from 5000 m to 6000 m to determine environmental, spatial and seasonal factors structuring bacterial communities characterized by 16 S rRNA gene deep sequencing. Dust traps and fresh-snow samples were used to assess dust abundance and viability, community structure and abundance of dust associated microbial communities. Significantly different habitats among the altitude-transect samples corresponded to both phylogenetically distant and closely-related communities at distances as short as 50 m showing high community spatial divergence. High within-group variability that was related to an order of magnitude higher dust deposition obscured seasonal and temporal rearrangements in microbial communities. Although dust particle and associated cell deposition rates were highly correlated, seasonal dust communities of bacteria were distinct and differed significantly from recipient soil communities. Analysis of closest relatives to dust OTUs, HYSPLIT back-calculation of airmass trajectories and small dust particle size (4–12 µm) suggested that the deposited dust and microbes came from distant continental, lacustrine and marine sources, e.g. Sahara, India, Caspian Sea and Tibetan plateau. Cyanobacteria represented less than 0.5% of microbial communities suggesting that the microbial communities benefitted from (co)deposited carbon which was reflected in the psychrotolerant nature of dust-particle associated bacteria. Conclusions/Significance The spatial, environmental and temporal complexity of the high-altitude soils of the Himalaya generates ongoing disturbance and colonization events that subject heterogeneous microniches to stochastic colonization by far away dust associated microbes and result in the observed spatially divergent bacterial communities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background The Himalaya with its altitude and geographical position forms a barrier to atmospheric transport, which produces much aqueous-particle monsoon precipitation and makes it the largest continuous ice-covered area outside polar regions. There is a paucity of data on high-altitude microbial communities, their native environments and responses to environmental-spatial variables relative to seasonal and deglaciation events. Methodology/Principal Findings Soils were sampled along altitude transects from 5000 m to 6000 m to determine environmental, spatial and seasonal factors structuring bacterial communities characterized by 16 S rRNA gene deep sequencing. Dust traps and fresh-snow samples were used to assess dust abundance and viability, community structure and abundance of dust associated microbial communities. Significantly different habitats among the altitude-transect samples corresponded to both phylogenetically distant and closely-related communities at distances as short as 50 m showing high community spatial divergence. High within-group variability that was related to an order of magnitude higher dust deposition obscured seasonal and temporal rearrangements in microbial communities. Although dust particle and associated cell deposition rates were highly correlated, seasonal dust communities of bacteria were distinct and differed significantly from recipient soil communities. Analysis of closest relatives to dust OTUs, HYSPLIT back-calculation of airmass trajectories and small dust particle size (4–12 µm) suggested that the deposited dust and microbes came from distant continental, lacustrine and marine sources, e.g. Sahara, India, Caspian Sea and Tibetan plateau. Cyanobacteria represented less than 0.5% of microbial communities suggesting that the microbial communities benefitted from (co)deposited carbon which was reflected in the psychrotolerant nature of dust-particle associated bacteria. Conclusions/Significance The spatial, environmental and temporal complexity of the high-altitude soils of the Himalaya generates ongoing disturbance and colonization events that subject heterogeneous microniches to stochastic colonization by far away dust associated microbes and result in the observed spatially divergent bacterial communities. |
Murovec, Boštjan; Perš, Janez; Mandeljc, Rok; Kenk, Vildana Sulić; Kovačič, Stanislav Towards commoditized smart-camera design Journal Article In: Journal of Systems Architecture, vol. 59, no. 10, Part A, pp. 847 - 858, 2013, ISSN: 1383-7621, (Smart Camera Architecture). @article{MUROVEC2013847,
title = {Towards commoditized smart-camera design},
author = {Boštjan Murovec and Janez Perš and Rok Mandeljc and Vildana Sulić Kenk and Stanislav Kovačič},
url = {http://www.sciencedirect.com/science/article/pii/S1383762113000799},
doi = {https://doi.org/10.1016/j.sysarc.2013.05.010},
issn = {1383-7621},
year = {2013},
date = {2013-01-01},
journal = {Journal of Systems Architecture},
volume = {59},
number = {10, Part A},
pages = {847 - 858},
abstract = {We propose a set of design principles for a cost-effective embedded smart camera. Our aim is to alleviate the shortcomings of the existing designs, such as excessive reliance on battery power and wireless networking, over-emphasized focus on specific use cases, and use of specialized technologies. In our opinion, these shortcomings prevent widespread commercialization and adoption of embedded smart cameras, especially in the context of visual-sensor networks. The proposed principles lead to a distinctively different design, which relies on commoditized, standardized and widely-available components, tools and knowledge. As an example of using these principles in practice, we present a smart camera, which is inexpensive, easy to build and support, capable of high-speed communication and enables rapid transfer of computer-vision algorithms to the embedded world.},
note = {Smart Camera Architecture},
keywords = {Commoditized smart camera, Design principles, General-purpose smart camera, Reference design, Visual-sensor networks},
pubstate = {published},
tppubtype = {article}
}
We propose a set of design principles for a cost-effective embedded smart camera. Our aim is to alleviate the shortcomings of the existing designs, such as excessive reliance on battery power and wireless networking, over-emphasized focus on specific use cases, and use of specialized technologies. In our opinion, these shortcomings prevent widespread commercialization and adoption of embedded smart cameras, especially in the context of visual-sensor networks. The proposed principles lead to a distinctively different design, which relies on commoditized, standardized and widely-available components, tools and knowledge. As an example of using these principles in practice, we present a smart camera, which is inexpensive, easy to build and support, capable of high-speed communication and enables rapid transfer of computer-vision algorithms to the embedded world. |
2012
|
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Robust 3D Face Recognition Journal Article In: Electrotechnical Review, vol. 79, no. 1-2, pp. 1-6, 2012. @article{Križaj-EV-2012,
title = {Robust 3D Face Recognition},
author = {Janez Križaj and Vitomir Štruc and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/robust3dfacerecognition/},
year = {2012},
date = {2012-06-01},
urldate = {2012-06-01},
journal = {Electrotechnical Review},
volume = {79},
number = {1-2},
pages = {1-6},
abstract = {Face recognition in uncontrolled environments is hindered by variations in illumination, pose, expression and occlusions of faces. Many practical face-recognition systems are affected by these variations. One way to increase the robustness to illumination and pose variations is to use 3D facial images. In this paper 3D face-recognition systems are presented. Their structure and operation are described. The robustness of such systems to variations in uncontrolled environments is emphasized. We present some preliminary results of a system developed in our laboratory.},
keywords = {3d face recognition, biometrics, gaussian mixture models, GMM, modeling},
pubstate = {published},
tppubtype = {article}
}
Face recognition in uncontrolled environments is hindered by variations in illumination, pose, expression and occlusions of faces. Many practical face-recognition systems are affected by these variations. One way to increase the robustness to illumination and pose variations is to use 3D facial images. In this paper 3D face-recognition systems are presented. Their structure and operation are described. The robustness of such systems to variations in uncontrolled environments is emphasized. We present some preliminary results of a system developed in our laboratory. |
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Towards robust 3D face verification using Gaussian mixture models Journal Article In: International Journal of Advanced Robotic Systems, vol. 9, 2012. @article{krizaj2012towards,
title = {Towards robust 3D face verification using Gaussian mixture models},
author = {Janez Križaj and Vitomir Štruc and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/en/towardsrobust3dfaceverificationusinggaussianmixturemodels/},
doi = {10.5772/52200},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {International Journal of Advanced Robotic Systems},
volume = {9},
publisher = {InTech},
abstract = {This paper focuses on the use of Gaussian Mixture models (GMM) for 3D face verification. A special interest is taken in practical aspects of 3D face verification systems, where all steps of the verification procedure need to be automated and no meta-data, such as pre-annotated eye/nose/mouth positions, is available to the system. In such settings the performance of the verification system correlates heavily with the performance of the employed alignment (i.e., geometric normalization) procedure. We show that popular holistic as well as local recognition techniques, such as principal component analysis (PCA), or Scale-invariant feature transform (SIFT)-based methods considerably deteriorate in their performance when an “imperfect” geometric normalization procedure is used to align the 3D face scans and that in these situations GMMs should be preferred. Moreover, several possibilities to improve the performance and robustness of the classical GMM framework are presented and evaluated: i) explicit inclusion of spatial information, during the GMM construction procedure, ii) implicit inclusion of spatial information during the GMM construction procedure and iii) on-line evaluation and possible rejection of local feature vectors based on their likelihood. We successfully demonstrate the feasibility of the proposed modifications on the Face Recognition Grand Challenge data set.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper focuses on the use of Gaussian Mixture models (GMM) for 3D face verification. A special interest is taken in practical aspects of 3D face verification systems, where all steps of the verification procedure need to be automated and no meta-data, such as pre-annotated eye/nose/mouth positions, is available to the system. In such settings the performance of the verification system correlates heavily with the performance of the employed alignment (i.e., geometric normalization) procedure. We show that popular holistic as well as local recognition techniques, such as principal component analysis (PCA), or Scale-invariant feature transform (SIFT)-based methods considerably deteriorate in their performance when an “imperfect” geometric normalization procedure is used to align the 3D face scans and that in these situations GMMs should be preferred. Moreover, several possibilities to improve the performance and robustness of the classical GMM framework are presented and evaluated: i) explicit inclusion of spatial information, during the GMM construction procedure, ii) implicit inclusion of spatial information during the GMM construction procedure and iii) on-line evaluation and possible rejection of local feature vectors based on their likelihood. We successfully demonstrate the feasibility of the proposed modifications on the Face Recognition Grand Challenge data set. |
Vesnicer, Bostjan; Gros, Jerneja Žganec; Pavešić, Nikola; Štruc, Vitomir Face recognition using simplified probabilistic linear discriminant analysis Journal Article In: International Journal of Advanced Robotic Systems, vol. 9, 2012. @article{vesnicer2012face,
title = {Face recognition using simplified probabilistic linear discriminant analysis},
author = {Bostjan Vesnicer and Jerneja Žganec Gros and Nikola Pavešić and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/en/facerecognitionusingsimplifiedprobabilisticlineardiscriminantanalysis/},
doi = {10.5772/52258},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {International Journal of Advanced Robotic Systems},
volume = {9},
publisher = {InTech},
abstract = {Face recognition in uncontrolled environments remains an open problem that has not been satisfactorily solved by existing recognition techniques. In this paper, we tackle this problem using a variant of the recently proposed Probabilistic Linear Discriminant Analysis (PLDA). We show that simplified versions of the PLDA model, which are regularly used in the field of speaker recognition, rely on certain assumptions that not only result in a simpler PLDA model, but also reduce the computational load of the technique and - as indicated by our experimental assessments - improve recognition performance. Moreover, we show that, contrary to the general belief that PLDA-based methods produce well calibrated verification scores, score normalization techniques can still deliver significant performance gains, but only if non-parametric score normalization techniques are employed. Last but not least, we demonstrate the competitiveness of the simplified PLDA model for face recognition by comparing our results with the state-of-the-art results from the literature obtained on the second version of the large-scale Face Recognition Grand Challenge (FRGC) database.},
keywords = {biometrics, face recognition, plda, simplified PLDA},
pubstate = {published},
tppubtype = {article}
}
Face recognition in uncontrolled environments remains an open problem that has not been satisfactorily solved by existing recognition techniques. In this paper, we tackle this problem using a variant of the recently proposed Probabilistic Linear Discriminant Analysis (PLDA). We show that simplified versions of the PLDA model, which are regularly used in the field of speaker recognition, rely on certain assumptions that not only result in a simpler PLDA model, but also reduce the computational load of the technique and - as indicated by our experimental assessments - improve recognition performance. Moreover, we show that, contrary to the general belief that PLDA-based methods produce well calibrated verification scores, score normalization techniques can still deliver significant performance gains, but only if non-parametric score normalization techniques are employed. Last but not least, we demonstrate the competitiveness of the simplified PLDA model for face recognition by comparing our results with the state-of-the-art results from the literature obtained on the second version of the large-scale Face Recognition Grand Challenge (FRGC) database. |