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Shuffled Patch-Wise Supervision for Presentation Attack Detection

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2021

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Gesellschaft für Informatik e.V.

Zusammenfassung

Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets ---Replay-Mobile, OULU-NPU--- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.

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Kantarcı, Alperen; Dertli, Hasan; Ekenel, Hazım Kemal (2021): Shuffled Patch-Wise Supervision for Presentation Attack Detection. BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-709-8. pp. 61-70. Regular Research Papers. International Digital Conference. 15.-17. September 2021

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