2018
|
Grm, Klemen; Štruc, Vitomir Deep face recognition for surveillance applications Journal Article In: IEEE Intelligent Systems, vol. 33, no. 3, pp. 46–50, 2018. @article{GrmIEEE2018,
title = {Deep face recognition for surveillance applications},
author = {Klemen Grm and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/UniversityOfLjubljana_IEEE_IS_Submission.pdf},
year = {2018},
date = {2018-05-01},
journal = {IEEE Intelligent Systems},
volume = {33},
number = {3},
pages = {46--50},
abstract = {Automated person recognition from surveillance quality footage is an open research problem with many potential application areas. In this paper, we aim at addressing this problem by presenting a face recognition approach tailored towards surveillance applications. The presented approach is based on domain-adapted convolutional neural networks and ranked second in the International Challenge on Biometric Recognition in the Wild (ICB-RW) 2016. We evaluate the performance of the presented approach on part of the Quis-Campi dataset and compare it against several existing face recognition techniques and one (state-of-the-art) commercial system. We find that the domain-adapted convolutional network outperforms all other assessed techniques, but is still inferior to human performance.},
keywords = {biometrics, face, face recognition, performance evaluation, surveillance},
pubstate = {published},
tppubtype = {article}
}
Automated person recognition from surveillance quality footage is an open research problem with many potential application areas. In this paper, we aim at addressing this problem by presenting a face recognition approach tailored towards surveillance applications. The presented approach is based on domain-adapted convolutional neural networks and ranked second in the International Challenge on Biometric Recognition in the Wild (ICB-RW) 2016. We evaluate the performance of the presented approach on part of the Quis-Campi dataset and compare it against several existing face recognition techniques and one (state-of-the-art) commercial system. We find that the domain-adapted convolutional network outperforms all other assessed techniques, but is still inferior to human performance. |
2017
|
Klemen, Grm; Simon, Dobrišek; Vitomir, Štruc Evaluating image superresolution algorithms for cross-resolution face recognition Proceedings Article In: Proceedings of the Twenty-sixth International Electrotechnical and Computer Science Conference ERK 2017, 2017. @inproceedings{ERK2017Grm,
title = {Evaluating image superresolution algorithms for cross-resolution face recognition},
author = {Grm Klemen and Dobrišek Simon and Štruc Vitomir},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/review_submission.pdf},
year = {2017},
date = {2017-09-01},
booktitle = {Proceedings of the Twenty-sixth International Electrotechnical and Computer Science Conference ERK 2017},
abstract = {With recent advancements in deep learning and convolutional neural networks (CNNs), face recognition has seen significant performance improvements over the last few years. However, low-resolution images still remain challenging, with CNNs performing relatively poorly compared to humans. One possibility to improve performance in these settings often advocated in the literature is the use of super-resolution (SR). In this paper, we explore the usefulness of SR algorithms for cross-resolution face recognition in experiments on the Labeled Faces in the Wild (LFW) and SCface datasets using four recent deep CNN models. We conduct experiments with synthetically down-sampled images as well as real-life low-resolution imagery captured by surveillance cameras. Our experiments show that image super-resolution can improve face recognition performance considerably on very low-resolution images (of size 24 x 24 or 32 x 32 pixels), when images are artificially down-sampled, but has a lesser (or sometimes even a detrimental) effect with real-life images leaving significant room for further research in this area.},
keywords = {face, face hallucination, face recognition, performance evaluation, super-resolution},
pubstate = {published},
tppubtype = {inproceedings}
}
With recent advancements in deep learning and convolutional neural networks (CNNs), face recognition has seen significant performance improvements over the last few years. However, low-resolution images still remain challenging, with CNNs performing relatively poorly compared to humans. One possibility to improve performance in these settings often advocated in the literature is the use of super-resolution (SR). In this paper, we explore the usefulness of SR algorithms for cross-resolution face recognition in experiments on the Labeled Faces in the Wild (LFW) and SCface datasets using four recent deep CNN models. We conduct experiments with synthetically down-sampled images as well as real-life low-resolution imagery captured by surveillance cameras. Our experiments show that image super-resolution can improve face recognition performance considerably on very low-resolution images (of size 24 x 24 or 32 x 32 pixels), when images are artificially down-sampled, but has a lesser (or sometimes even a detrimental) effect with real-life images leaving significant room for further research in this area. |
Emersic, Ziga; Meden, Blaz; Peer, Peter; Struc, Vitornir Covariate analysis of descriptor-based ear recognition techniques Proceedings Article In: 2017 international conference and workshop on bioinspired intelligence (IWOBI), pp. 1–9, IEEE 2017. @inproceedings{emersic2017covariate,
title = {Covariate analysis of descriptor-based ear recognition techniques},
author = {Ziga Emersic and Blaz Meden and Peter Peer and Vitornir Struc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Covariate_Analysis_of_Descriptor_based_Ear_Recognition_Techniques.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {2017 international conference and workshop on bioinspired intelligence (IWOBI)},
pages = {1--9},
organization = {IEEE},
abstract = {Dense descriptor-based feature extraction techniques represent a popular choice for implementing biometric ear recognition system and are in general considered to be the current state-of-the-art in this area. In this paper, we study the impact of various factors (i.e., head rotation, presence of occlusions, gender and ethnicity) on the performance of 8 state-of-the-art descriptor-based ear recognition techniques. Our goal is to pinpoint weak points of the existing technology and identify open problems worth exploring in the future. We conduct our covariate analysis through identification experiments on the challenging AWE (Annotated Web Ears) dataset and report our findings. The results of our study show that high degrees of head movement and presence of accessories significantly impact the identification performance, whereas mild degrees of the listed factors and other covariates such as gender and ethnicity impact the identification performance only to a limited extent.},
keywords = {AWE, covariate analysis, descriptors, ear, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Dense descriptor-based feature extraction techniques represent a popular choice for implementing biometric ear recognition system and are in general considered to be the current state-of-the-art in this area. In this paper, we study the impact of various factors (i.e., head rotation, presence of occlusions, gender and ethnicity) on the performance of 8 state-of-the-art descriptor-based ear recognition techniques. Our goal is to pinpoint weak points of the existing technology and identify open problems worth exploring in the future. We conduct our covariate analysis through identification experiments on the challenging AWE (Annotated Web Ears) dataset and report our findings. The results of our study show that high degrees of head movement and presence of accessories significantly impact the identification performance, whereas mild degrees of the listed factors and other covariates such as gender and ethnicity impact the identification performance only to a limited extent. |
2016
|
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. |
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. |
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. |
2015
|
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. |
2014
|
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 signicantly 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 verication 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 signicantly 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 verication 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. |
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. |
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. |
2010
|
Štruc, Vitomir; Pavešić, Nikola Face recogniton from color images using sparse projection analysis Proceedings Article In: Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010), pp. 445-453, Povoa de Varzim, Portugal, 2010. @inproceedings{ICIAR2010_Sparse,
title = {Face recogniton from color images using sparse projection analysis},
author = {Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/facerecognitonfromcolorimagesusingsparseprojectionanalysis/},
year = {2010},
date = {2010-06-01},
urldate = {2010-06-01},
booktitle = {Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010)},
pages = {445-453},
address = {Povoa de Varzim, Portugal},
abstract = {The paper presents a novel feature extraction technique for face recognition which uses sparse projection axes to compute a lowdimensional representation of face images. The proposed technique derives the sparse axes by first recasting the problem of face recognition as a regression problem and then solving the new (under-determined) regression problem by computing the solution with minimum L1 norm. The developed technique, named Sparse Projection Analysis (SPA), is applied to color as well as grey-scale images from the XM2VTS database and compared to popular subspace projection techniques (with sparse and dense projection axes) from the literature. The results of the experimental assessment show that the proposed technique ensures promising results on un-occluded as well occluded images from the XM2VTS database.},
keywords = {biometrics, face verification, ICIAR, performance evaluation, sparse projection analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents a novel feature extraction technique for face recognition which uses sparse projection axes to compute a lowdimensional representation of face images. The proposed technique derives the sparse axes by first recasting the problem of face recognition as a regression problem and then solving the new (under-determined) regression problem by computing the solution with minimum L1 norm. The developed technique, named Sparse Projection Analysis (SPA), is applied to color as well as grey-scale images from the XM2VTS database and compared to popular subspace projection techniques (with sparse and dense projection axes) from the literature. The results of the experimental assessment show that the proposed technique ensures promising results on un-occluded as well occluded images from the XM2VTS database. |
Križaj, Janez; Štruc, Vitomir; Pavešić, Nikola Adaptation of SIFT Features for Robust Face Recognition Proceedings Article In: Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010), pp. 394-404, Povoa de Varzim, Portugal, 2010. @inproceedings{ICIAR2010_Sift,
title = {Adaptation of SIFT Features for Robust Face Recognition},
author = {Janez Križaj and Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/adaptationofsiftfeaturesforrobustfacerecognition/},
year = {2010},
date = {2010-06-01},
urldate = {2010-06-01},
booktitle = {Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010)},
pages = {394-404},
address = {Povoa de Varzim, Portugal},
abstract = {The Scale Invariant Feature Transform (SIFT) is an algorithm used to detect and describe scale-, translation- and rotation-invariant local features in images. The original SIFT algorithm has been successfully applied in general object detection and recognition tasks, panorama stitching and others. One of its more recent uses also includes face recognition, where it was shown to deliver encouraging results. SIFT-based face recognition techniques found in the literature rely heavily on the so-called keypoint detector, which locates interest points in the given image that are ultimately used to compute the SIFT descriptors. While these descriptors are known to be among others (partially) invariant to illumination changes, the keypoint detector is not. Since varying illumination is one of the main issues affecting the performance of face recognition systems, the keypoint detector represents the main source of errors in face recognition systems relying on SIFT features. To overcome the presented shortcoming of SIFT-based methods, we present in this paper a novel face recognition technique that computes the SIFT descriptors at predefined (fixed) locations learned during the training stage. By doing so, it eliminates the need for keypoint detection on the test images and renders our approach more robust to illumination changes than related approaches from the literature. Experiments, performed on the Extended Yale B face database, show that the proposed technique compares favorably with several popular techniques from the literature in terms of performance.},
keywords = {biometrics, dense SIFT, face recognition, performance evaluation, SIFT, SIFT features},
pubstate = {published},
tppubtype = {inproceedings}
}
The Scale Invariant Feature Transform (SIFT) is an algorithm used to detect and describe scale-, translation- and rotation-invariant local features in images. The original SIFT algorithm has been successfully applied in general object detection and recognition tasks, panorama stitching and others. One of its more recent uses also includes face recognition, where it was shown to deliver encouraging results. SIFT-based face recognition techniques found in the literature rely heavily on the so-called keypoint detector, which locates interest points in the given image that are ultimately used to compute the SIFT descriptors. While these descriptors are known to be among others (partially) invariant to illumination changes, the keypoint detector is not. Since varying illumination is one of the main issues affecting the performance of face recognition systems, the keypoint detector represents the main source of errors in face recognition systems relying on SIFT features. To overcome the presented shortcoming of SIFT-based methods, we present in this paper a novel face recognition technique that computes the SIFT descriptors at predefined (fixed) locations learned during the training stage. By doing so, it eliminates the need for keypoint detection on the test images and renders our approach more robust to illumination changes than related approaches from the literature. Experiments, performed on the Extended Yale B face database, show that the proposed technique compares favorably with several popular techniques from the literature in terms of performance. |
Štruc, Vitomir; Dobrišek, Simon; Pavešić, Nikola Confidence Weighted Subspace Projection Techniques for Robust Face Recognition in the Presence of Partial Occlusions Proceedings Article In: Proceedings of the International Conference on Pattern Recognition (ICPR'10), pp. 1334-1338, Istanbul, Turkey, 2010. @inproceedings{ICPR_Struc_2010,
title = {Confidence Weighted Subspace Projection Techniques for Robust Face Recognition in the Presence of Partial Occlusions},
author = {Vitomir Štruc and Simon Dobrišek and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/confidenceweightedsubspaceprojectiontechniquesforrobustfacerecognitioninthepresenceofpartialocclusions/},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR'10)},
pages = {1334-1338},
address = {Istanbul, Turkey},
keywords = {biometrics, face recognition, face verification, ICPR, performance evaluation, subspace projection},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2009
|
Štruc, Vitomir; Gajšek, Rok; Pavešić, Nikola Principal Gabor Filters for Face Recognition Proceedings Article In: Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Systems and Applications (BTAS'09), pp. 1-6, IEEE, Washington D.C., U.S.A., 2009. @inproceedings{BTAS2009,
title = {Principal Gabor Filters for Face Recognition},
author = {Vitomir Štruc and Rok Gajšek and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/principalgaborfiltersforfacerecognition/},
doi = {10.1109/BTAS.2009.5339020},
year = {2009},
date = {2009-09-01},
urldate = {2009-09-01},
booktitle = {Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Systems and Applications (BTAS'09)},
pages = {1-6},
publisher = {IEEE},
address = {Washington D.C., U.S.A.},
abstract = {Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance of recognition techniques presented in the literature exploits these filters to achieve robust face recognition. However, while exhibiting desirable properties, such as orientational selectivity or spatial locality, Gabor filters have also some shortcomings which crucially affect the characteristics and size of the Gabor representation of a given face pattern. Amongst these shortcomings the fact that the filters are not orthogonal one to another and are, hence, correlated is probably the most important. This makes the information contained in the Gabor face representation redundant and also affects the size of the representation. To overcome this problem we propose in this paper to employ orthonormal linear combinations of the original Gabor filters rather than the filters themselves for deriving the Gabor face representation. The filters, named principal Gabor filters for the fact that they are computed by means of principal component analysis, are assessed in face recognition experiments performed on the XM2VTS and YaleB databases, where encouraging results are achieved.},
keywords = {biometrics, face verification, feature extraction, Gabor features, performance evaluation, principal Gabor filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance of recognition techniques presented in the literature exploits these filters to achieve robust face recognition. However, while exhibiting desirable properties, such as orientational selectivity or spatial locality, Gabor filters have also some shortcomings which crucially affect the characteristics and size of the Gabor representation of a given face pattern. Amongst these shortcomings the fact that the filters are not orthogonal one to another and are, hence, correlated is probably the most important. This makes the information contained in the Gabor face representation redundant and also affects the size of the representation. To overcome this problem we propose in this paper to employ orthonormal linear combinations of the original Gabor filters rather than the filters themselves for deriving the Gabor face representation. The filters, named principal Gabor filters for the fact that they are computed by means of principal component analysis, are assessed in face recognition experiments performed on the XM2VTS and YaleB databases, where encouraging results are achieved. |
Štruc, Vitomir; Ma, Zongmin; Pavešić, Nikola Nuisance Attribute Projection in the Logarithm Domain for Face Recognition under Severe Illumination Changes Proceedings Article In: Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09), pp. 279-281, Portorož, Slovenia, 2009. @inproceedings{ERK2009N,
title = {Nuisance Attribute Projection in the Logarithm Domain for Face Recognition under Severe Illumination Changes},
author = {Vitomir Štruc and Zongmin Ma and Nikola Pavešić},
year = {2009},
date = {2009-09-01},
booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09)},
pages = {279-281},
address = {Portorož, Slovenia},
keywords = {biometrics, face verification, illumination changes, illumination invariance, nuisance attribute projection, performance evaluation, robust recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Štruc, Vitomir; Ma, Zongmin; Pavešić, Nikola Face Recognition using Sparse Projection Axes Proceedings Article In: Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09), pp. 271-274, Portorož, Slovenia, 2009. @inproceedings{ERK2009S,
title = {Face Recognition using Sparse Projection Axes},
author = {Vitomir Štruc and Zongmin Ma and Nikola Pavešić},
year = {2009},
date = {2009-09-01},
booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09)},
pages = {271-274},
address = {Portorož, Slovenia},
keywords = {biometrics, erk, face recognition, face verification, performance evaluation, sparse projection analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Štruc, Vitomir; Pavešić, Nikola A comparative assessment of appearance based feature extraction techniques and their susceptibility to image degradations in face recognition systems Proceedings Article In: Proceedings of the International Conference on Machine Learning and Pattern Recognition (ICMLPR'09), pp. 326-334, Paris, France, 2009. @inproceedings{FSKD208b,
title = {A comparative assessment of appearance based feature extraction techniques and their susceptibility to image degradations in face recognition systems},
author = {Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/acomparativeassessmentofappearancebasedfeatureextractiontechniquesandtheirsusceptibilitytoimagedegradationsinfacerecognitionsystems/},
year = {2009},
date = {2009-06-01},
urldate = {2009-06-01},
booktitle = {Proceedings of the International Conference on Machine Learning and Pattern Recognition (ICMLPR'09)},
volume = {54},
pages = {326-334},
address = {Paris, France},
abstract = {Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in ”real-life” operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.},
keywords = {biometrics, face recognition, face verification, image degradations, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in ”real-life” operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases. |
Štruc, Vitomir; Pavešić, Nikola A comparison of feature normalization techniques for PCA-based palmprint recognition Proceedings Article In: Proceedings of the International Conference on Mathematical Modeling (MATHMOD'09), pp. 2450-2453, Viena, Austria, 2009. @inproceedings{Mathmod09,
title = {A comparison of feature normalization techniques for PCA-based palmprint recognition},
author = {Vitomir Štruc and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/acomparisonoffeaturenormalizationtechniquesforpca-basedpalmprintrecognition/},
year = {2009},
date = {2009-02-01},
urldate = {2009-02-01},
booktitle = {Proceedings of the International Conference on Mathematical Modeling (MATHMOD'09)},
pages = {2450-2453},
address = {Viena, Austria},
abstract = {Computing user templates (or models) for biometric authentication systems is one of the most crucial steps towards efficient and accurate biometric recognition. The constructed templates should encode user specific information extracted from a sample of a given biometric modality, such as, for example, palmprints, and exhibit a sufficient level of dissimilarity with other templates stored in the systems database. Clearly, the characteristics of the user templates depend on the approach employed for the extraction of biometric features, as well as on the procedure used to normalize the extracted feature vectors. While feature-extraction methods are a well studied topic, for which a vast amount of comparative studies can be found in the literature, normalization techniques lack such studies and are only briefly mentioned in most cases. In this paper we, therefore, apply several normalization techniques to feature vectors extracted from palmprint images by means of principal component analysis (PCA) and perform a comparative analysis on the results. We show that the choice of an appropriate normalization technique greatly influences the performance of the palmprint-based authentication system and can result in error rate reductions of more than 30%.},
keywords = {biometrics, face verification, feature normalization, normalization, pca, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Computing user templates (or models) for biometric authentication systems is one of the most crucial steps towards efficient and accurate biometric recognition. The constructed templates should encode user specific information extracted from a sample of a given biometric modality, such as, for example, palmprints, and exhibit a sufficient level of dissimilarity with other templates stored in the systems database. Clearly, the characteristics of the user templates depend on the approach employed for the extraction of biometric features, as well as on the procedure used to normalize the extracted feature vectors. While feature-extraction methods are a well studied topic, for which a vast amount of comparative studies can be found in the literature, normalization techniques lack such studies and are only briefly mentioned in most cases. In this paper we, therefore, apply several normalization techniques to feature vectors extracted from palmprint images by means of principal component analysis (PCA) and perform a comparative analysis on the results. We show that the choice of an appropriate normalization technique greatly influences the performance of the palmprint-based authentication system and can result in error rate reductions of more than 30%. |
Štruc, Vitomir; Pavešić, Nikola Image normalization techniques for robust face recognition Proceedings Article In: Proceedings of the International Conference on Signal Processing, Robotics and Automation (ISPRA'09), pp. 155-160, Cambridge, UK, 2009. @inproceedings{ISPRA09,
title = {Image normalization techniques for robust face recognition},
author = {Vitomir Štruc and Nikola Pavešić},
year = {2009},
date = {2009-02-01},
booktitle = {Proceedings of the International Conference on Signal Processing, Robotics and Automation (ISPRA'09)},
pages = {155-160},
address = {Cambridge, UK},
keywords = {biometrics, face recognition, face verification, histogram remapping, performance evaluation, preprocessing},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Poh, Norman; Chan, Chi Ho; Kittler, Josef; Marcel, Sebastien; McCool, Christopher; Argones-Rua, Enrique; Alba-Castro, Jose Luis; Villegas, Mauricio; Paredes, Roberto; Štruc, Vitomir; Pavešić, Nikola; Salah, Albert Ali; Fang, Hui; Costen, Nicholas Face Video Competition Proceedings Article In: Tistarelli, Massimo; Nixon, Mark (Ed.): Proceedings of the international Conference on Biometrics (ICB), pp. 715-724, Springer-Verlag, Berlin, Heidelberg, 2009. @inproceedings{ICB2009,
title = {Face Video Competition},
author = {Norman Poh and Chi Ho Chan and Josef Kittler and Sebastien Marcel and Christopher McCool and Enrique Argones-Rua and Jose Luis Alba-Castro and Mauricio Villegas and Roberto Paredes and Vitomir Štruc and Nikola Pavešić and Albert Ali Salah and Hui Fang and Nicholas Costen},
editor = {Massimo Tistarelli and Mark Nixon},
url = {https://lmi.fe.uni-lj.si/en/facevideocompetition/},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of the international Conference on Biometrics (ICB)},
volume = {5558},
pages = {715-724},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
series = {Lecture Notes on Computer Science},
abstract = {Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.},
keywords = {biometrics, competition, face recognition, face verification, ICB, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes. |
Gajšek, Rok; Štruc, Vitomir; Dobrišek, Simon; Žibert, Janez; Mihelič, France; Pavešić, Nikola Combining audio and video for detection of spontaneous emotions Proceedings Article In: Biometric ID management and multimodal communication, pp. 114-121, Springer-Verlag, Berlin, Heidelberg, 2009. @inproceedings{BioID_Multi2009b,
title = {Combining audio and video for detection of spontaneous emotions},
author = {Rok Gajšek and Vitomir Štruc and Simon Dobrišek and Janez Žibert and France Mihelič and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/combiningaudioandvideofordetectionofspontaneousemotions/},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Biometric ID management and multimodal communication},
volume = {5707},
pages = {114-121},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
series = {Lecture Notes on Computer Science},
abstract = {The paper presents our initial attempts in building an audio video emotion recognition system. Both, audio and video sub-systems are discussed, and description of the database of spontaneous emotions is given. The task of labelling the recordings from the database according to different emotions is discussed and the measured agreement between multiple annotators is presented. Instead of focusing on the prosody in audio emotion recognition, we evaluate the possibility of using linear transformations (CMLLR) as features. The classification results from audio and video sub-systems are combined using sum rule fusion and the increase in recognition results, when using both modalities, is presented.},
keywords = {emotion recognition, facial expression recognition, performance evaluation, speech processing, speech technologies},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents our initial attempts in building an audio video emotion recognition system. Both, audio and video sub-systems are discussed, and description of the database of spontaneous emotions is given. The task of labelling the recordings from the database according to different emotions is discussed and the measured agreement between multiple annotators is presented. Instead of focusing on the prosody in audio emotion recognition, we evaluate the possibility of using linear transformations (CMLLR) as features. The classification results from audio and video sub-systems are combined using sum rule fusion and the increase in recognition results, when using both modalities, is presented. |
2008
|
Štruc, Vitomir; Vesnicer, Boštjan; Pavešić, Nikola The phase-based Gabor Fisher classifier and its application to face recognition under varying illumination conditions Proceedings Article In: Proceedings of the IEEE International Conference on Signal Processing and Communication Systems (ICSPCS'08), pp. 1-6, IEEE, Gold Coast, Australia, 2008, ISBN: 978-1-4244-4243-0. @inproceedings{ICSPCS08,
title = {The phase-based Gabor Fisher classifier and its application to face recognition under varying illumination conditions},
author = {Vitomir Štruc and Boštjan Vesnicer and Nikola Pavešić},
doi = {10.1109/ICSPCS.2008.4813663},
isbn = {978-1-4244-4243-0},
year = {2008},
date = {2008-12-01},
booktitle = {Proceedings of the IEEE International Conference on Signal Processing and Communication Systems (ICSPCS'08)},
pages = {1-6},
publisher = {IEEE},
address = {Gold Coast, Australia},
abstract = {The paper introduces a feature extraction technique for face recognition called the phase-based Gabor Fisher classifier (PBGFC). The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. The feasibility of the proposed method was assessed in a series of face verification experiments performed on the XM2VTS database. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as principal component analysis (PCA), the Fisherface method or the DCT-mod2 approach, while it ensures similar verification performance as the established Gabor Fisher Classifier (GFC). The results also show that the proposed phase-based Gabor Fisher classifier performs the best among the tested methods when severe illumination changes are introduced to the face images.},
keywords = {biometrics, face verification, feature extraction, Gabor features, performance evaluation, phase congruency features, phase features},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces a feature extraction technique for face recognition called the phase-based Gabor Fisher classifier (PBGFC). The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. The feasibility of the proposed method was assessed in a series of face verification experiments performed on the XM2VTS database. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as principal component analysis (PCA), the Fisherface method or the DCT-mod2 approach, while it ensures similar verification performance as the established Gabor Fisher Classifier (GFC). The results also show that the proposed phase-based Gabor Fisher classifier performs the best among the tested methods when severe illumination changes are introduced to the face images. |
Štruc, Vitomir; Mihelič, France; Gajšek, Rok; Pavešić, Nikola Regression techniques versus discriminative methods for face recognition Proceedings Article In: Proceedings of the 9th international PhD Workshop on Systems and Control, pp. 1-5, Izola, Slovenia, 2008. @inproceedings{PHD2008,
title = {Regression techniques versus discriminative methods for face recognition},
author = {Vitomir Štruc and France Mihelič and Rok Gajšek and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/regressiontechniquesversusdiscriminativemethodsforfacerecognition/},
year = {2008},
date = {2008-10-01},
urldate = {2008-10-01},
booktitle = {Proceedings of the 9th international PhD Workshop on Systems and Control},
pages = {1-5},
address = {Izola, Slovenia},
abstract = {In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discriminative techniques still exhibit some shortcomings when compared to the expressive approaches. More specifically, they suffer from the so-called small sample size (SSS) problem which is regularly encountered in the field of face recognition and occurs when the sample dimensionality is larger than the number of available training samples per subject. In this type of problems, the discriminative techniques need modifications in order to be feasible, but even in their most elaborate forms require at least two training samples per subject. The expressive approaches, on the other hand, are not susceptible to the SSS problem and are thus applicable even in the most extreme case of the small sample size problem, i.e., when only one training sample per subject is available. Nevertheless, in this paper we will show that the recognition performance of the expressive methods can match (or in some cases surpass) that of the discriminative techniques if the expressive feature extraction approaches are used as multivariate regression techniques with a pre-designed response matrix that encodes the class membership of the training samples. The effectiveness of the regression techniques for face recognition is demonstrated in a series of experiments performed on the ORL database. Additionally a comparative assessment of the regression techniques and popular discriminative approaches is presented.},
keywords = {biometrics, face recognition, face verification, modeling, performance evaluation, regression techniques},
pubstate = {published},
tppubtype = {inproceedings}
}
In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discriminative techniques still exhibit some shortcomings when compared to the expressive approaches. More specifically, they suffer from the so-called small sample size (SSS) problem which is regularly encountered in the field of face recognition and occurs when the sample dimensionality is larger than the number of available training samples per subject. In this type of problems, the discriminative techniques need modifications in order to be feasible, but even in their most elaborate forms require at least two training samples per subject. The expressive approaches, on the other hand, are not susceptible to the SSS problem and are thus applicable even in the most extreme case of the small sample size problem, i.e., when only one training sample per subject is available. Nevertheless, in this paper we will show that the recognition performance of the expressive methods can match (or in some cases surpass) that of the discriminative techniques if the expressive feature extraction approaches are used as multivariate regression techniques with a pre-designed response matrix that encodes the class membership of the training samples. The effectiveness of the regression techniques for face recognition is demonstrated in a series of experiments performed on the ORL database. Additionally a comparative assessment of the regression techniques and popular discriminative approaches is presented. |
Štruc, Vitomir; Mihelič, France; Pavešić, Nikola Combining experts for improved face verification performance Proceedings Article In: Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'08), pp. 233-236, Portorož, Slovenia, 2008. @inproceedings{ERK2008,
title = {Combining experts for improved face verification performance},
author = {Vitomir Štruc and France Mihelič and Nikola Pavešić},
url = {https://lmi.fe.uni-lj.si/en/combiningexpertsforimprovedfaceverificationperformance/},
year = {2008},
date = {2008-09-01},
urldate = {2008-09-01},
booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'08)},
pages = {233-236},
address = {Portorož, Slovenia},
abstract = {Samodejno razpoznavanje (avtentikacija/identifikacija) obrazov predstavlja eno najaktivnejših raziskovalnih področij biometrije. Avtentikacija oz. identifikacija oseb z razpoznavanjem obrazov ponuja možen način povečanja varnosti pri različnih dejavnostih, (npr. pri elektronskem poslovanju na medmrežju, pri bančnih storitvah ali pri vstopu v določene prostore, stavbe in države). Ponuja univerzalen in nevsiljiv način razpoznavanja oseb, ki pa trenutno še ni dovolj zanesljiv. Kot možna rešitev problema zanesljivosti razpoznavanja se v literaturi vse pogosteje pojavljajo večmodalni pristopi, v katerih se razpoznavanje izvede na podlagi večjega števila postopkov razpoznavanja obrazov. V skladu z opisanim trendom, bomo v članku ovrednotili zanesljivost delovanja različnih postopkov razpoznavanja obrazov, ki jih bomo na koncu združili še v večmodalni pristop. S pomočjo eksperimentov na podatkovni zbirki XM2VTS bomo preverili zanesljivost delovanja večmodalnega pristopa in jo primerjali z zanesljivostjo uveljavljenih postopkov razpoznavanja.},
keywords = {biometrics, erk, face recognition, face verification, fusion, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
Samodejno razpoznavanje (avtentikacija/identifikacija) obrazov predstavlja eno najaktivnejših raziskovalnih področij biometrije. Avtentikacija oz. identifikacija oseb z razpoznavanjem obrazov ponuja možen način povečanja varnosti pri različnih dejavnostih, (npr. pri elektronskem poslovanju na medmrežju, pri bančnih storitvah ali pri vstopu v določene prostore, stavbe in države). Ponuja univerzalen in nevsiljiv način razpoznavanja oseb, ki pa trenutno še ni dovolj zanesljiv. Kot možna rešitev problema zanesljivosti razpoznavanja se v literaturi vse pogosteje pojavljajo večmodalni pristopi, v katerih se razpoznavanje izvede na podlagi večjega števila postopkov razpoznavanja obrazov. V skladu z opisanim trendom, bomo v članku ovrednotili zanesljivost delovanja različnih postopkov razpoznavanja obrazov, ki jih bomo na koncu združili še v večmodalni pristop. S pomočjo eksperimentov na podatkovni zbirki XM2VTS bomo preverili zanesljivost delovanja večmodalnega pristopa in jo primerjali z zanesljivostjo uveljavljenih postopkov razpoznavanja. |
Štruc, Vitomir; Pavešić, Nikola A palmprint verification system based on phase congruency features Proceedings Article In: Schouten, Ben; Juul, Niels Christian; Drygajlo, Andrzej; Tistarelli, Massimo (Ed.): Biometrics and Identity Management, pp. 110-119, Springer-Verlag, Berlin, Heidelberg, 2008. @inproceedings{BioID2008,
title = {A palmprint verification system based on phase congruency features},
author = {Vitomir Štruc and Nikola Pavešić},
editor = {Ben Schouten and Niels Christian Juul and Andrzej Drygajlo and Massimo Tistarelli},
url = {https://lmi.fe.uni-lj.si/en/apalmprintverificationsystembasedonphasecongruencyfeatures/},
doi = {10.1007/978-3-540-89991-4_12},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Biometrics and Identity Management},
volume = {5372},
pages = {110-119},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
series = {Lecture Notes on Computer Science},
abstract = {The paper presents a fully automatic palmprint verification system which uses 2D phase congruency to extract line features from a palmprint image and subsequently performs linear discriminant analysis on the computed line features to represent them in a more compact manner. The system was trained and tested on a database of 200 people (2000 hand images) and achieved a false acceptance rate (FAR) of 0.26% and a false rejection rate (FRR) of 1.39% in the best performing verification experiment. In a comparison, where in addition to the proposed system, three popular palmprint recognition techniques were tested for their verification accuracy, the proposed system performed the best.},
keywords = {feature extraction, palmprint recognition, palmprint verification, palmprints, performance evaluation},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents a fully automatic palmprint verification system which uses 2D phase congruency to extract line features from a palmprint image and subsequently performs linear discriminant analysis on the computed line features to represent them in a more compact manner. The system was trained and tested on a database of 200 people (2000 hand images) and achieved a false acceptance rate (FAR) of 0.26% and a false rejection rate (FRR) of 1.39% in the best performing verification experiment. In a comparison, where in addition to the proposed system, three popular palmprint recognition techniques were tested for their verification accuracy, the proposed system performed the best. |