Abstract
The rapid deployment of facial biometric system has raised attention about their vulnerability to presentation attacks (PAs). Currently, due to the feature extraction capability of convolution neural network (CNN), it has achieved excellent results in most multi-modal face anti-spoofing (FAS) algorithms. Similarly, we proposed multi-modal FAS using Channel Cross Fusion Network (CCFN) and Depth-wise Convolution (GDConv), FaceBagNets for short. The CCFN is utilized to cross-fuse multi-modal feature by using the pairwise cross approach before fusing multi-modal feature in the channel direction, and the GDConv replaces the global average pooling (GAP) to raise the performance. We also utilized the patch-based strategy to obtain richer feature, the random model feature erasing (RMFE) strategy to prevent the over-fitting and the squeeze-and-excitation network (SE-NET) to focus on key feature. Finally, we conducted extensive experiments on two multi-modal datasets, then verified the effectiveness of the CCFN and the GDConv. Much advanced results were acquired and outperformed most state-of-the-art methods.
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Acknowledgment
Science and Technology to Boost the Economy 2020 Key Project (SQ2020YFF0410766), Scientific Research Foundation of Southwest University (SWU2008045) and Chongqing Technology Innovation and Application Development Project (cstc2020jscx-msxmX0147).
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Zhou, Q., Yang, M., Chen, S., Tang, M., Wang, X. (2022). Multi-modal Face Anti-spoofing Using Channel Cross Fusion Network and Global Depth-Wise Convolution. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_35
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