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 ; Struc, Vitomir ; Peer, Peter ; Khiarak, Jalil Nourmohammadi ; Jaryani, Farhang ; Nasab, Samaneh Salehi ; Moafinejad, Seyed Naeim ; Amini, Yasin ; Noshad, Morteza (2021):
NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization.
IEEE, 2021 IEEE International Joint Conference on Biometrics (IJCB), virtual Conference, 04.-07.08.2021, ISBN 978-1-6654-3780-6,
DOI: 10.1109/IJCB52358.2021.9484336,
[Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2021 |
| Creators: | 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 ; Struc, Vitomir ; Peer, Peter ; Khiarak, Jalil Nourmohammadi ; Jaryani, Farhang ; Nasab, Samaneh Salehi ; Moafinejad, Seyed Naeim ; Amini, Yasin ; Noshad, Morteza |
| Title: | NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization |
| Language: | English |
| 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. |
| Publisher: | IEEE |
| ISBN: | 978-1-6654-3780-6 |
| Uncontrolled Keywords: | Biometrics, Deep learning, Machine learning, Iris recognition, Image segmentation |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems 20 Department of Computer Science > Mathematical and Applied Visual Computing |
| Event Title: | 2021 IEEE International Joint Conference on Biometrics (IJCB) |
| Event Location: | virtual Conference |
| Event Dates: | 04.-07.08.2021 |
| Date Deposited: | 03 Aug 2021 13:10 |
| DOI: | 10.1109/IJCB52358.2021.9484336 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Send an inquiry |
Options (only for editors)
 |
Show editorial Details |
Drucken
Impressum