Face anti-spoofing based on ASFF and LBP self-supervision branch

Junjie Ren; Yi Zhang; Ruisheng Zhang; Jianfeng Luo

doi:10.1117/12.2684525

1 August 2023 Face anti-spoofing based on ASFF and LBP self-supervision branch

Junjie Ren, Yi Zhang, Ruisheng Zhang, Jianfeng Luo

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542H (2023) https://doi.org/10.1117/12.2684525
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing approaches ignore context information and local information about faces, this paper proposes a face anti-spoofing algorithm that fuses multi-scale features and texture features of images. On the one hand, we extract multi-scale features using adaptively spatial feature fusion (ASFF) and polarized self-attention (PSA) mechanism. On the other hand, local binary patterns (LBP) is used to form an auxiliary self-supervision branch to extract image texture difference information. As a final step, end-to-end live detection is achieved by balancing the difference between two different loss functions based on homoscedastic uncertainty. The EER of the presented method is 0.81% and 1.26% on CASIA-FASD and MSU-MFSD, respectively, and the ACER is only 1.64% and 2.07%±1.05% on the second and third protocols of Oulu_NPU, which indicates the validity of the method.

Citation Download Citation

Junjie Ren, Yi Zhang, Ruisheng Zhang, and Jianfeng Luo "Face anti-spoofing based on ASFF and LBP self-supervision branch", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542H (1 August 2023); https://doi.org/10.1117/12.2684525

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