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.
2021 IEEE International Joint Conference on Biometrics (IJCB). virtual Conference (04.-07.08.2021)
doi: 10.1109/IJCB52358.2021.9484343
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network |
| Sprache: | Englisch |
| Publikationsjahr: | 20 Juli 2021 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2021 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 04.-07.08.2021 |
| DOI: | 10.1109/IJCB52358.2021.9484343 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Deep learning, Machine learning, Spoofing attacks, Iris recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 03 Aug 2021 07:19 |
| Letzte Änderung: | 03 Aug 2021 07:19 |
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