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NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization

 
: 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, Andrés; 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; Struc, Vitomir; Peer, Peter; Khiarak, Jalil Nourmohammadi; Jaryani, Farhang; Nasab, Samaneh Salehi; Moafinejad, Seyed Naeim; Amini, Yasin; Noshad, Morteza

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Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Biometrics Council; International Association for Pattern Recognition -IAPR-:
IEEE International Joint Conference on Biometrics, IJCB 2021 : 4-7 August 2021, Shenzhen, China, virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-3781-3
ISBN: 978-1-6654-3780-6
Art. 9484336, 10 S.
International Joint Conference on Biometrics (IJCB) <2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
ATHENE
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Lead Topic: Digitized Work; Lead Topic: Smart City; Research Line: Computer vision (CV); Research Line: Machine Learning (ML); biometrics; deep learning; machine learning; Iris recognition; image segmentation; ATHENE; CRISP

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.

: http://publica.fraunhofer.de/dokumente/N-638537.html