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

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Computer Vision – ECCV 2022 (ECCV 2022)

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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 anti-spoofing. 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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Authors and Affiliations

  1. University of California, Merced, USA

    Hsin-Ping Huang, Taihong Xiao & Ming-Hsuan Yang

  2. Google Research, Mountain View, USA

    Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Jinwei Yuan, Hartwig Adam & Ming-Hsuan Yang

  3. Yonsei University, Seoul, South Korea

    Ming-Hsuan Yang

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  1. Hsin-Ping Huang
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  2. Deqing Sun
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  3. Yaojie Liu
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Correspondence to Hsin-Ping Huang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Huang, HP. et al. (2022). Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-19778-9_3

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