Generative Domain Adaptation for Face Anti-Spoofing

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.

arXiv commentAccepted to European Conference on Computer Vision (ECCV), 2022

References in corpus (10)

• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
• CyCADA: Cycle-Consistent Adversarial Domain Adaptation
• DMT: Dynamic Mutual Training for Semi-Supervised Learning
• Context-Aware Mixup for Domain Adaptive Semantic Segmentation
• Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
• Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
• PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
• Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
• Deep Transfer Across Domains for Face Anti-spoofing
• Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling

Cited by in corpus (7)

• A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
• Context-Aware Mixup for Domain Adaptive Semantic Segmentation
• Instance-Aware Domain Generalization for Face Anti-Spoofing
• Rethinking Domain Generalization: Discriminability and Generalizability
• Domain-Generalized Face Anti-Spoofing with Unknown Attacks
• Rethinking Impersonation and Dodging Attacks on Face Recognition Systems
• Single Domain Dynamic Generalization for Iris Presentation Attack Detection