Abstract
Face anti-spoofing in unconstrained environment is one of the key issues in face biometric based authentication and security applications. To minimize the false alarms in face anti-spoofing tests, this paper proposes a novel approach to learn perturbed feature maps by perturbing the convolutional feature maps with Histogram of Oriented Gradients (HOG) features. The perturbed feature maps are learned simultaneously during training of Convolution Neural Network (CNN) for face anti-spoofing, in an end-to-end fashion. Extensive experiments are performed on state-of-the-art face anti-spoofing databases, like OULU-NPU, CASIA-FASD and Replay-Attack, in both intra-database and cross-database scenarios. Experimental results indicate that the proposed framework perform significantly better compare to previous state-of-the-art approaches in both intra-database and cross-database face anti-spoofing scenarios.
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The work in this paper was supported by City University of Hong Kong under the research project with grant number 7004430.
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Rehman, Y.A.U., Po, LM., Liu, M., Zou, Z., Ou, W. (2020). Perturbing Convolutional Feature Maps with Histogram of Oriented Gradients for Face Liveness Detection. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_1
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