Category-Conditional Gradient Alignment for Domain Adaptive Face Anti-Spoofing

In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and target domains. To this end, this work develops a category-conditional gradient alignment guided face anti-spoofing algorithm (CCGA-FAS) from a novel perspective of gradient discrepancy elimination. Technically, the category-conditional gradient alignment mechanism maximizes the cosine similarity of the gradient vectors generated by source and target samples within the live and spoof categories separately, which promotes the source and target domains to follow similar gradient descent directions during optimization. Considering that the gradient vector generation and alignment is computationally dependent on reliable category information, a temporal knowledge and flexible threshold based dynamic category measurer is devised to provide pseudo category information for unlabelled target samples in an easy-to-hard manner. The optimization for CCGA-FAS is implemented under the teacher-student structure, where the student model serves as the gradient optimization backbone, and the category prediction simultaneously benefits from the teacher and student models to consolidate the alignment stability. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods in both unsupervised and K-shot semi-supervised domain adaptive face anti-spoofing scenarios.