2021
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Wang, Caiyong; Wang, Yunlong; Zhang, Kunbo; Muhammad, Jawad; Lu, Tianhao; Zhang, Qi; Tian, Qichuan; He, Zhaofeng; Sun, Zhenan; Zhang, Yiwen; Liu, Tianbao; Yang, Wei; Wu, Dongliang; Liu, Yingfeng; Zhou, Ruiye; Wu, Huihai; Zhang, Hao; Wang, Junbao; Wang, Jiayi; Xiong, Wantong; Shi, Xueyu; Zeng, Shao; Li, Peihua; Sun, Haodong; Wang, Jing; Zhang, Jiale; Wang, Qi; Wu, Huijie; Zhang, Xinhui; Li, Haiqing; Chen, Yu; Chen, Liang; Zhang, Menghan; Sun, Ye; Zhou, Zhiyong; Boutros, Fadi; Damer, Naser; Kuijper, Arjan; Tapia, Juan; Valenzuela, Andres; Busch, Christoph; Gupta, Gourav; Raja, Kiran; Wu, Xi; Li, Xiaojie; Yang, Jingfu; Jing, Hongyan; Wang, Xin; Kong, Bin; Yin, Youbing; Song, Qi; Lyu, Siwei; Hu, Shu; Premk, Leon; Vitek, Matej; Štruc, Vitomir; Peer, Peter; Khiarak, Jalil Nourmohammadi; Jaryani, Farhang; Nasab, Samaneh Salehi; Moafinejad, Seyed Naeim; Amini, Yasin; Noshad, Morteza NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization Proceedings Article In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. @inproceedings{NIR_IJCB2021,
title = {NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization},
author = {Caiyong Wang and Yunlong Wang and Kunbo Zhang and Jawad Muhammad and Tianhao Lu and Qi Zhang and Qichuan Tian and Zhaofeng He and Zhenan Sun and Yiwen Zhang and Tianbao Liu and Wei Yang and Dongliang Wu and Yingfeng Liu and Ruiye Zhou and Huihai Wu and Hao Zhang and Junbao Wang and Jiayi Wang and Wantong Xiong and Xueyu Shi and Shao Zeng and Peihua Li and Haodong Sun and Jing Wang and Jiale Zhang and Qi Wang and Huijie Wu and Xinhui Zhang and Haiqing Li and Yu Chen and Liang Chen and Menghan Zhang and Ye Sun and Zhiyong Zhou and Fadi Boutros and Naser Damer and Arjan Kuijper and Juan Tapia and Andres Valenzuela and Christoph Busch and Gourav Gupta and Kiran Raja and Xi Wu and Xiaojie Li and Jingfu Yang and Hongyan Jing and Xin Wang and Bin Kong and Youbing Yin and Qi Song and Siwei Lyu and Shu Hu and Leon Premk and Matej Vitek and Vitomir Štruc and Peter Peer and Jalil Nourmohammadi Khiarak and Farhang Jaryani and Samaneh Salehi Nasab and Seyed Naeim Moafinejad and Yasin Amini and Morteza Noshad},
url = {https://ieeexplore.ieee.org/iel7/9484326/9484328/09484336.pdf?casa_token=FOKx4ltO-hYAAAAA:dCkNHfumDzPGkAipRdbppNWpzAiUYUrJL6OrAjNmimTxUA0Vmx311-3-J3ej7YQc_zONxEO-XKo},
doi = {10.1109/IJCB52358.2021.9484336},
year = {2021},
date = {2021-08-01},
booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021)},
abstract = {For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.},
keywords = {biometrics, competition, iris, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research. |
2019
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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
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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. |
2017
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Lavrič, Primož; Emeršič, Žiga; Meden, Blaž; Štruc, Vitomir; Peer, Peter Do it Yourself: Building a Low-Cost Iris Recognition System at Home Using Off-The-Shelf Components Proceedings Article In: Electrotechnical and Computer Science Conference ERK 2017, 2017. @inproceedings{ERK2017,
title = {Do it Yourself: Building a Low-Cost Iris Recognition System at Home Using Off-The-Shelf Components},
author = {Primož Lavrič and Žiga Emeršič and Blaž Meden and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/lavricdo_it.pdf},
year = {2017},
date = {2017-09-01},
booktitle = {Electrotechnical and Computer Science Conference ERK 2017},
abstract = {Among the different biometric traits that can be used for person recognition, the human iris is generally consid-ered to be among the most accurate. However, despite a plethora of desirable characteristics, iris recognition is not widely as widely used as competing biometric modalities likely due to the high cost of existing commercial iris-recognition systems. In this paper we contribute towards the availability of low-cost iris recognition systems and present a prototype system built using off-the-shelf components. We describe the prototype device, the pipeline used for iris recognition, evaluate the performance of our solution on a small in-house dataset and discuss directions for future work. The current version of our prototype includes complete hardware and software implementations and has a combined bill-of-materials of 110 EUR.
},
keywords = {biometrics, iris, sensor design},
pubstate = {published},
tppubtype = {inproceedings}
}
Among the different biometric traits that can be used for person recognition, the human iris is generally consid-ered to be among the most accurate. However, despite a plethora of desirable characteristics, iris recognition is not widely as widely used as competing biometric modalities likely due to the high cost of existing commercial iris-recognition systems. In this paper we contribute towards the availability of low-cost iris recognition systems and present a prototype system built using off-the-shelf components. We describe the prototype device, the pipeline used for iris recognition, evaluate the performance of our solution on a small in-house dataset and discuss directions for future work. The current version of our prototype includes complete hardware and software implementations and has a combined bill-of-materials of 110 EUR.
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