Abstract
Face liveness detection is essential to ensuring the security of face recognition systems. Most current models rely on convolutional neural networks to achieve domain generalization through complete representations on common modules. The limitations of receptive field prevent model getting global context and capturing long-range dependencies, which ignores the global face semantic information and lacks the local focus of the face on more fine-grained features. To tackle these challenges, this paper proposes a silent face liveness detection domain generalization model based on the fusion of convolutional neural network (CNN) and Swin Transformer features, namely, CLCSN. Then, a contrastive learning technique is suggested to highlight liveness-related style aspects, which improves the generalization capacity, in order to generate a generalized representation. Experimental results demonstrate our approach’s effectiveness in solving the face liveness detection domain generalization problems.
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Acknowledgments
his work was supported by research fundings from National Science Foundation of China (No.82303675, No.61272315 and No.12305404), the National Key R&D Program of China (No.2023YFF0613504), Zhejiang Provincial Major Science and Technology Project (No.2023C01040), Natural Science Foundation of Zhejiang Province (No. LY21F020028, No. LY22F010010 and No. LQ22F020021), Fundamental Research Funds for the Provincial Universities of Zhejiang (No. 2022YW52) and National Platform for basic conditions of science and technology (No.APT2301–7).
Disclosure of Interests. Disclosure of interests. the authors have no competing interests to declare that are relevant to the content of this article.
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Tang, Y. et al. (2024). Contrastive Learning for Silent Face Liveness Detection Based on A Hybrid Framework. In: Huang, DS., Zhang, C., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14868. Springer, Singapore. https://doi.org/10.1007/978-981-97-5600-1_3
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DOI: https://doi.org/10.1007/978-981-97-5600-1_3
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