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Lightweight network-based multi-modal feature fusion for face anti-spoofing

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Abstract

Effectively identifying attacked faces is an urgent problem to be solved in the scenario of face recognition. As deep learning is applied to face anti-spoofing (FAS), many multi-modal approaches have been proven to be more efficient than single-modal, but at the same time, multi-modal approaches require a huge number of parameters and therefore result in high computation. To tackle this problem, a lightweight network-based multi-modal FAS model was proposed, which took patch-level images from multi-modal images (YCbCr, Depth, and IR) as the input to different branches, and designed a lightweight feature extraction module to solve the redundancy of feature maps extracted by the filters. Finally, an attention-based feature fusion module was constructed to fuse and classify the features extracted by each branch network. A great number of comparative experimental results demonstrated that this method greatly reduced parameters at a high accuracy. For example, the accuracy on single-modal datasets (Replay-Attack and CASIA-FASD) is 100%, and that on multi-modal dataset (CASIA-SURF) is 98.1269% (TPR@FPR = 10e−4) and 0.2232%. In addition, the backbone network has only 0.25 M parameters and 0.37 G FLOPs.

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Acknowledgments

This work was supported by the Science and Technology Research Project of Chongqing Education Commission (Grant No. KJQN201900833), and the Scientific Research and Innovation Foundation of Chongqing, China (Grant No. CYS21398).

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Authors and Affiliations

  1. School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, 400067, China

    Dan He, Xiping He, Rui Yuan & Yue Li

  2. Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing, 400067, China

    Xiping He

  3. School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Chao Shen

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  1. Dan He
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He, D., He, X., Yuan, R. et al. Lightweight network-based multi-modal feature fusion for face anti-spoofing. Vis Comput 39, 1423–1435 (2023). https://doi.org/10.1007/s00371-022-02420-6

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