2023
|
Vitek, Matej; Das, Abhijit; Lucio, Diego Rafael; Jr., Luiz Antonio Zanlorensi; Menotti, David; Khiarak, Jalil Nourmohammadi; Shahpar, Mohsen Akbari; Asgari-Chenaghlu, Meysam; Jaryani, Farhang; Tapia, Juan E.; Valenzuela, Andres; Wang, Caiyong; Wang, Yunlong; He, Zhaofeng; Sun, Zhenan; Boutros, Fadi; Damer, Naser; Grebe, Jonas Henry; Kuijper, Arjan; Raja, Kiran; Gupta, Gourav; Zampoukis, Georgios; Tsochatzidis, Lazaros; Pratikakis, Ioannis; Kumar, S. V. Aruna; Harish, B. S.; Pal, Umapada; Peer, Peter; Štruc, Vitomir Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach Journal Article In: IEEE Transactions on Information Forensics and Security, vol. 18, pp. 190-205, 2023, ISSN: 1556-6013. @article{TIFS_Sclera2022,
title = {Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach},
author = {Matej Vitek and Abhijit Das and Diego Rafael Lucio and Luiz Antonio Zanlorensi Jr. and David Menotti and Jalil Nourmohammadi Khiarak and Mohsen Akbari Shahpar and Meysam Asgari-Chenaghlu and Farhang Jaryani and Juan E. Tapia and Andres Valenzuela and Caiyong Wang and Yunlong Wang and Zhaofeng He and Zhenan Sun and Fadi Boutros and Naser Damer and Jonas Henry Grebe and Arjan Kuijper and Kiran Raja and Gourav Gupta and Georgios Zampoukis and Lazaros Tsochatzidis and Ioannis Pratikakis and S. V. Aruna Kumar and B. S. Harish and Umapada Pal and Peter Peer and Vitomir Štruc},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926136},
doi = {10.1109/TIFS.2022.3216468},
issn = {1556-6013},
year = {2023},
date = {2023-01-18},
urldate = {2022-10-18},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {18},
pages = {190-205},
abstract = {Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.},
keywords = {bias, biometrics, fairness, group evaluation, ocular, sclera, sclera segmentation, segmentation},
pubstate = {published},
tppubtype = {article}
}
Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias. |
2022
|
Tomašević, Darian; Peer, Peter; Štruc, Vitomir BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Proceedings Article In: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) , pp. 1-10, 2022. @inproceedings{TomasevicIJCBBiOcular,
title = {BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images},
author = {Darian Tomašević and Peter Peer and Vitomir Štruc },
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/12/BiModal_StyleGAN.pdf
https://arxiv.org/pdf/2205.01536.pdf},
year = {2022},
date = {2022-10-20},
urldate = {2022-10-20},
booktitle = {IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) },
pages = {1-10},
abstract = {Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN.},
keywords = {biometrics, CNN, data synthesis, deep learning, ocular, segmentation, StyleGAN, synthetic data},
pubstate = {published},
tppubtype = {inproceedings}
}
Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN. |
2020
|
Vitek, M.; Das, A.; Pourcenoux, Y.; Missler, A.; Paumier, C.; Das, S.; Ghosh, I. De; Lucio, D. R.; Jr., L. A. Zanlorensi; Menotti, D.; Boutros, F.; Damer, N.; Grebe, J. H.; Kuijper, A.; Hu, J.; He, Y.; Wang, C.; Liu, H.; Wang, Y.; Sun, Z.; Osorio-Roig, D.; Rathgeb, C.; Busch, C.; Tapia, J.; Valenzuela, A.; Zampoukis, G.; Tsochatzidis, L.; Pratikakis, I.; Nathan, S.; Suganya, R.; Mehta, V.; Dhall, A.; Raja, K.; Gupta, G.; Khiarak, J. N.; Akbari-Shahper, M.; Jaryani, F.; Asgari-Chenaghlu, M.; Vyas, R.; Dakshit, S.; Dakshit, S.; Peer, P.; Pal, U.; Štruc, V. SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment Proceedings Article In: International Joint Conference on Biometrics (IJCB 2020), pp. 1–10, 2020. @inproceedings{SSBC2020,
title = {SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment},
author = {M. Vitek and A. Das and Y. Pourcenoux and A. Missler and C. Paumier and S. Das and I. De Ghosh and D. R. Lucio and L. A. Zanlorensi Jr. and D. Menotti and F. Boutros and N. Damer and J. H. Grebe and A. Kuijper and J. Hu and Y. He and C. Wang and H. Liu and Y. Wang and Z. Sun and D. Osorio-Roig and C. Rathgeb and C. Busch and J. Tapia and A.~Valenzuela and G. Zampoukis and L. Tsochatzidis and I. Pratikakis and S. Nathan and R. Suganya and V. Mehta and A. Dhall and K. Raja and G. Gupta and J. N. Khiarak and M. Akbari-Shahper and F. Jaryani and M. Asgari-Chenaghlu and R. Vyas and S. Dakshit and S. Dakshit and P. Peer and U. Pal and V. Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/IJCB_SSBC_2020.pdf},
year = {2020},
date = {2020-09-28},
booktitle = {International Joint Conference on Biometrics (IJCB 2020)},
pages = {1--10},
abstract = {The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. },
keywords = {biometrics, competition IJCB, ocular, sclera, segmentation, SSBC},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. |
Vitek, Matej; Rot, Peter; Struc, Vitomir; Peer, Peter A comprehensive investigation into sclera biometrics: a novel dataset and performance study Journal Article In: Neural Computing and Applications, pp. 1-15, 2020. @article{vitek2020comprehensive,
title = {A comprehensive investigation into sclera biometrics: a novel dataset and performance study},
author = {Matej Vitek and Peter Rot and Vitomir Struc and Peter Peer},
url = {https://link.springer.com/epdf/10.1007/s00521-020-04782-1},
doi = {https://doi.org/10.1007/s00521-020-04782-1},
year = {2020},
date = {2020-01-01},
journal = {Neural Computing and Applications},
pages = {1-15},
abstract = {The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general.},
keywords = {biometrics, CNN, dataset, multi-view, ocular, performance study, recognition, sclera, segmentation, visible light},
pubstate = {published},
tppubtype = {article}
}
The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general. |
2019
|
Rot, Peter; Vitek, Matej; Grm, Klemen; Emeršič, Žiga; Peer, Peter; Štruc, Vitomir Deep Sclera Segmentation and Recognition Book Section In: Uhl, Andreas; Busch, Christoph; Marcel, Sebastien; Veldhuis, Rainer (Ed.): Handbook of Vascular Biometrics, pp. 395-432, Springer, 2019, ISBN: 978-3-030-27731-4. @incollection{ScleraNetChapter,
title = {Deep Sclera Segmentation and Recognition},
author = {Peter Rot and Matej Vitek and Klemen Grm and Žiga Emeršič and Peter Peer
and Vitomir Štruc},
editor = {Andreas Uhl and Christoph Busch and Sebastien Marcel and Rainer Veldhuis},
url = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-27731-4_13.pdf},
doi = {https://doi.org/10.1007/978-3-030-27731-4_13},
isbn = {978-3-030-27731-4},
year = {2019},
date = {2019-11-14},
booktitle = {Handbook of Vascular Biometrics},
pages = {395-432},
publisher = {Springer},
chapter = {13},
series = {Advances in Computer Vision and Pattern Recognition},
abstract = {In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance.},
keywords = {biometrics, CNN, deep learning, ocular, sclera, segmentation, vasculature},
pubstate = {published},
tppubtype = {incollection}
}
In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance. |
Lozej, Juš; Štepec, Dejan; Štruc, Vitomir; Peer, Peter Influence of segmentation on deep iris recognition performance Proceedings Article In: 7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2019), 2019. @inproceedings{lozej2019influence,
title = {Influence of segmentation on deep iris recognition performance},
author = {Juš Lozej and Dejan Štepec and Vitomir Štruc and Peter Peer},
url = {https://arxiv.org/pdf/1901.10431.pdf},
year = {2019},
date = {2019-03-01},
booktitle = {7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2019)},
journal = {arXiv preprint arXiv:1901.10431},
abstract = {Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings.},
keywords = {biometrics, iris, ocular, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings. |
2018
|
Rot, Peter; Emeršič, Žiga; Struc, Vitomir; Peer, Peter Deep multi-class eye segmentation for ocular biometrics Proceedings Article In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–8, IEEE 2018. @inproceedings{rot2018deep,
title = {Deep multi-class eye segmentation for ocular biometrics},
author = {Peter Rot and Žiga Emeršič and Vitomir Struc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/MultiClassReduced.pdf},
year = {2018},
date = {2018-07-01},
booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},
pages = {1--8},
organization = {IEEE},
abstract = {Segmentation techniques for ocular biometrics typically focus on finding a single eye region in the input image at the time. Only limited work has been done on multi-class eye segmentation despite a number of obvious advantages. In this paper we address this gap and present a deep multi-class eye segmentation model build around the SegNet architecture. We train the model on a small dataset (of 120 samples) of eye images and observe it to generalize well to unseen images and to ensure highly accurate segmentation results. We evaluate the model on the Multi-Angle Sclera Database (MASD) dataset and describe comprehensive experiments focusing on: i) segmentation performance, ii) error analysis, iii) the sensitivity of the model to changes in view direction, and iv) comparisons with competing single-class techniques. Our results show that the proposed model is viable solution for multi-class eye segmentation suitable for recognition (multi-biometric) pipelines based on ocular characteristics.},
keywords = {biometrics, eye, ocular, sclera, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Segmentation techniques for ocular biometrics typically focus on finding a single eye region in the input image at the time. Only limited work has been done on multi-class eye segmentation despite a number of obvious advantages. In this paper we address this gap and present a deep multi-class eye segmentation model build around the SegNet architecture. We train the model on a small dataset (of 120 samples) of eye images and observe it to generalize well to unseen images and to ensure highly accurate segmentation results. We evaluate the model on the Multi-Angle Sclera Database (MASD) dataset and describe comprehensive experiments focusing on: i) segmentation performance, ii) error analysis, iii) the sensitivity of the model to changes in view direction, and iv) comparisons with competing single-class techniques. Our results show that the proposed model is viable solution for multi-class eye segmentation suitable for recognition (multi-biometric) pipelines based on ocular characteristics. |
Lozej, Juš; Meden, Blaž; Struc, Vitomir; Peer, Peter End-to-end iris segmentation using U-Net Proceedings Article In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–6, IEEE 2018. @inproceedings{lozej2018end,
title = {End-to-end iris segmentation using U-Net},
author = {Juš Lozej and Blaž Meden and Vitomir Struc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/IWOBI_2018_paper_15.pdf},
year = {2018},
date = {2018-07-01},
booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},
pages = {1--6},
organization = {IEEE},
abstract = {Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area.},
keywords = {biometrics, CNN, convolutional neural networks, iris, ocular, U-net},
pubstate = {published},
tppubtype = {inproceedings}
}
Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area. |
Das, Abhijit; Pal, Umapada; Ferrer, Miguel A.; Blumenstein, Michael; Štepec, Dejan; Rot, Peter; Emeršič, Žiga; Peer, Peter; Štruc, Vitomir SSBC 2018: Sclera Segmentation Benchmarking Competition Proceedings Article In: 2018 International Conference on Biometrics (ICB), 2018. @inproceedings{Dasicb2018,
title = {SSBC 2018: Sclera Segmentation Benchmarking Competition},
author = {Abhijit Das and Umapada Pal and Miguel A. Ferrer and Michael Blumenstein and Dejan Štepec and Peter Rot and Žiga Emeršič and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/icb2018_sserbc.pdf},
year = {2018},
date = {2018-02-01},
booktitle = {2018 International Conference on Biometrics (ICB)},
abstract = {This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using .a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.},
keywords = {competition, ocular, sclera, sclera segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using .a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email. |