Abstract
With the development of face recognition system towards automation and unsupervised, illegal intruders have become a serious threat to face authentication system by disguising face authentication system, how to ensure the security of the face recognition system has become an urgent problem in face recognition technology. Therefore, living face detection has become an important issue that must be solved in the face authentication system. By deeply studying the importance of facial image color feature information for human face detection, a deep feature fusion network structure is constructed by deep convolutional neural networks ResNet and SENet to effectively train the involved face anti-spoof data. The feature with large amount of information, while suppressing the features with low usefulness, the experimental results are greatly improved compared with the traditional methods, and have higher recognition effect and accuracy.
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Acknowledgement
This study was supported by the National Natural Science Foundation of China (No. 61977005). The work was conducted at University of Science and Technology Beijing.
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Sun, J., Shi, Z. (2019). Face Anti-spoofing Algorithm Based on Depth Feature Fusion. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_21
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DOI: https://doi.org/10.1007/978-981-15-1925-3_21
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