Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing

• URL: http://arxiv.org/abs/2203.12175v2
• Date: Fri, 28 Jul 2023 18:21:00 GMT
• Title: Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
• Authors: Hsin-Ping Huang, Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Taihong Xiao, Jinwei Yuan, Hartwig Adam, Ming-Hsuan Yang
• Abstract summary: We present adaptive vision transformers (ViT) for robust cross-domain face antispoofing. We adopt ViT as a backbone to exploit its strength to account for long-range dependencies among pixels. Experiments on several benchmark datasets show that the proposed models achieve both robust and competitive performance.
• Score: 71.06718651013965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance. In this paper, we present adaptive vision transformers (ViT) for robust cross-domain face antispoofing. Specifically, we adopt ViT as a backbone to exploit its strength to account for long-range dependencies among pixels. We further introduce the ensemble adapters module and feature-wise transformation layers in the ViT to adapt to different domains for robust performance with a few samples. Experiments on several benchmark datasets show that the proposed models achieve both robust and competitive performance against the state-of-the-art methods for cross-domain face anti-spoofing using a few samples.

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