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Generative Domain Adaptation for Face Anti-Spoofing

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have drawn growing attention due to promising performances for target scenarios. Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features. However, insufficient supervision of unlabeled target domains and neglect of low-level feature alignment degrade the performances of existing methods. To address these issues, we propose a novel perspective of UDA FAS that directly fits the target data to the models, i.e., stylizes the target data to the source-domain style via image translation, and further feeds the stylized data into the well-trained source model for classification. The proposed Generative Domain Adaptation (GDA) framework combines two carefully designed consistency constraints: 1) Inter-domain neural statistic consistency guides the generator in narrowing the inter-domain gap. 2) Dual-level semantic consistency ensures the semantic quality of stylized images. Besides, we propose intra-domain spectrum mixup to further expand target data distributions to ensure generalization and reduce the intra-domain gap. Extensive experiments and visualizations demonstrate the effectiveness of our method against the state-of-the-art methods.

Q. Zhou and K.-Y. Zhang—Equal contributions.

Q. Zhou—Work done during an internship at Youtu Lab, Tencent.

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Acknowledgment

This work is supported by National Key Research and Development Program of China (2019YFC1521104), National Natural Science Foundation of China (72192821, 61972157), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), Shanghai Science and Technology Commission (21511101200, 22YF1420300), and Art major project of National Social Science Fund (I8ZD22).

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Correspondence to Shouhong Ding or Lizhuang Ma .

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Zhou, Q. et al. (2022). Generative Domain Adaptation for 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 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_20

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