Abstract
Face anti-spoofing (FAS) is an important part of the face recognition system. Although methods based on convolutional neural networks (CNN) have achieved great success, CNN may not be able to make good use of global information, resulting in the degradation of classification performance. Because Vision transformer (ViT) can use attention mechanisms to aggregate global information, some ViT based methods have been proposed. But most of these works treat the FAS problem as binary classification task, making it difficult to capture spoofing cues. In this work, we use ViT as our backbone. Then, we design an auxiliary supervised branch to exploit the depth information of the face image so that the algorithm can take the depth information into account when classifying. Cross domain experiments between CASIA-MFSD and Replay-Attack and intra experiments on OULU-NPU demonstrate the effectiveness of our method.
Thanks to No. 62101213, No. ZR2020QF107, No. ZR2020MF137, No. ZR2019MF040 for funding.
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Li, S., Dong, J., Chen, J., Gao, X., Niu, S. (2023). Vision Transformer with Depth Auxiliary Information for Face Anti-spoofing. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_29
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