Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss

• URL: http://arxiv.org/abs/2211.15955v1
• Date: Tue, 29 Nov 2022 06:28:00 GMT
• Title: Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss
• Authors: Chu-Chun Chuang, Chien-Yi Wang, Shang-Hong Lai
• Abstract summary: This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. Experiments on four public datasets demonstrate that the proposed framework and training strategies are more effective than previous works for model generalization to unseen domains.
• Score: 12.829618913069567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing variations of face presentation attacks, model generalization becomes an essential challenge for a practical face anti-spoofing system. This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. With the pixel-wise supervision from the face parsing and depth estimation tasks, the regularized features can better distinguish spoof faces. While simulating domain shift with meta-learning techniques, the proposed one-side triplet loss can further improve the generalization capability by a large margin. Extensive experiments on four public datasets demonstrate that the proposed framework and training strategies are more effective than previous works for model generalization to unseen domains.

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