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Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network

Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan (2021):
Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network.
IEEE, 2021 IEEE International Joint Conference on Biometrics (IJCB), virtual Conference, 04.-07.08.2021, ISBN 978-1-6654-3780-6,
DOI: 10.1109/IJCB52358.2021.9484343,
[Conference or Workshop Item]

Abstract

Iris presentation attack detection (PAD) plays a vital role in iris recognition systems. Most existing CNN-based iris PAD solutions 1) perform only binary label supervision during the training of CNNs, serving global information learning but weakening the capture of local discriminative features, 2) prefer the stacked deeper convolutions or expert-designed networks, raising the risk of overfitting, 3) fuse multiple PAD systems or various types of features, increasing difficulty for deployment on mobile devices. Hence, we propose a novel attention-based deep pixel-wise bi-nary supervision (A-PBS) method. Pixel-wise supervision is first able to capture the fine-grained pixel/patch-level cues. Then, the attention mechanism guides the network to automatically find regions that most contribute to an accurate PAD decision. Extensive experiments are performed on LivDet-Iris 2017 and three other publicly available databases to show the effectiveness and robustness of proposed A-PBS methods. For instance, the A-PBS model achieves an HTER of 6.50% on the IIITD-WVU database outperforming state-of-the-art methods.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan
Title: Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network
Language: English
Abstract:

Iris presentation attack detection (PAD) plays a vital role in iris recognition systems. Most existing CNN-based iris PAD solutions 1) perform only binary label supervision during the training of CNNs, serving global information learning but weakening the capture of local discriminative features, 2) prefer the stacked deeper convolutions or expert-designed networks, raising the risk of overfitting, 3) fuse multiple PAD systems or various types of features, increasing difficulty for deployment on mobile devices. Hence, we propose a novel attention-based deep pixel-wise bi-nary supervision (A-PBS) method. Pixel-wise supervision is first able to capture the fine-grained pixel/patch-level cues. Then, the attention mechanism guides the network to automatically find regions that most contribute to an accurate PAD decision. Extensive experiments are performed on LivDet-Iris 2017 and three other publicly available databases to show the effectiveness and robustness of proposed A-PBS methods. For instance, the A-PBS model achieves an HTER of 6.50% on the IIITD-WVU database outperforming state-of-the-art methods.

Publisher: IEEE
ISBN: 978-1-6654-3780-6
Uncontrolled Keywords: Biometrics, Deep learning, Machine learning, Spoofing attacks, Iris recognition
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 07:19
DOI: 10.1109/IJCB52358.2021.9484343
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