Multimodal Proxy-Free Face Anti-Spoofing Exploiting Local Patch Features

Face anti-spoofing (FAS) is vital to ensure the security of the face recognition systems, for which the essential task is to capture the unique spoof face features. Most of the existing methods extract spoof features from the whole faces, overlooking clues in local face patches. Meanwhile, researchers usually use intermediate parameters as a proxy in face classification, but this requires the design of additional loss functions. To solve these problems, we propose a multimodal proxy-free FAS model which uses contrastive language image pre-training (CLIP) as the backbone. Specifically, we use patches cropped from the original face to augment the data, forcing the network to learn local spoof features, such as the edges of printing attacks. At the same time, we introduce dynamic central difference convolutional (DCDC) adapter to extract fine-grained features in patches. Furthermore, we propose to adopt a proxy-free pairwise similarity learning (PSL) loss to achieve the goal that the maximum intra-class distance is smaller than the minimum inter-class distance. Experiments on several benchmark datasets show that the proposed method achieves state-of-the-art performance.