Shuffled Patch-Wise Supervision for Presentation Attack Detection

• URL: http://arxiv.org/abs/2109.03484v2
• Date: Thu, 9 Sep 2021 12:55:06 GMT
• Title: Shuffled Patch-Wise Supervision for Presentation Attack Detection
• Authors: Alperen Kantarc{\i}, Hasan Dertli, Haz{\i}m Kemal Ekenel
• Abstract summary: 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 presentation attack detection systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. We propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN.
• Score: 12.031796234206135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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