Multi-Modal Face Presentation Attack Detection via Spatial and Channel Attentions

摘要

Face presentation attack detection (PAD) has drawn increasing attentions to secure face recognition (FR) systems which are being widely used in many applications from access control to smartphone unlock. Traditional approaches for PAD may lack good generalization capability into new application scenarios due to the limited number of subjects and data modality. In this work, we propose an end-to-end multi-modal fusion approach via spatial and channel attention to improve PAD performance on CASIA-SURF. Specifically, We first build four branches integrated with spatial and channel attention module to obtain the uniform features of different modalities, i.e., RGB, Depth, IR and the fused modality with 9 channels which concatenating three modalities. Subsequently, the features extracted from the four branches are concatenated and fed into the shared layers to learn more discriminative features from the fusion perspective. Finally, we get the classification confidence scores w.r.t. PAD or not. The entire network is optimized with the joint of the center loss and softmax loss and SGRD solver to update the parameters. The proposed approach shows promising results on the CASIA-SURF dataset.