Generative Domain Adaptation for Face Anti-Spoofing
نویسندگان
چکیده
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 via aligning distribution of semantic high-level features. However, insufficient supervision unlabeled domains and neglect low-level feature alignment degrade methods. To address these issues, we propose a novel perspective that directly fits data models, i.e., stylizes source-domain style image translation, further feeds stylized into well-trained source model classification. The proposed Generative Domain Adaptation (GDA) framework combines two carefully designed consistency constraints: 1) Inter-domain neural statistic guides generator in narrowing inter-domain gap. 2) Dual-level ensures quality images. Besides, intra-domain spectrum mixup expand distributions ensure generalization reduce Extensive experiments visualizations demonstrate effectiveness our method against state-of-the-art
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-20065-6_20