Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing
This work addresses face presentation attacks, a security issue for biometric systems, but it is incremental as it builds on an existing method with new training strategies.
The authors tackled the problem of face anti-spoofing by enhancing a previously proposed LDCformer model with three novel training strategies, resulting in improved performance that outperforms previous methods as shown in extensive experiments.
Face anti-spoofing (FAS) heavily relies on identifying live/spoof discriminative features to counter face presentation attacks. Recently, we proposed LDCformer to successfully incorporate the Learnable Descriptive Convolution (LDC) into ViT, to model long-range dependency of locally descriptive features for FAS. In this paper, we propose three novel training strategies to effectively enhance the training of LDCformer to largely boost its feature characterization capability. The first strategy, dual-attention supervision, is developed to learn fine-grained liveness features guided by regional live/spoof attentions. The second strategy, self-challenging supervision, is designed to enhance the discriminability of the features by generating challenging training data. In addition, we propose a third training strategy, transitional triplet mining strategy, through narrowing the cross-domain gap while maintaining the transitional relationship between live and spoof features, to enlarge the domain-generalization capability of LDCformer. Extensive experiments show that LDCformer under joint supervision of the three novel training strategies outperforms previous methods.