Abstract
A modern facial recognition system cannot exist without anti-spoofing protection, such as protection from fake biometric samples. The most common way to get a face image is an optical camera. Due to the vast variability of the conditions for obtaining the picture, the problem is non-trivial. Currently, there is no out-of-the-box solution. In this article, we aim to combine different approaches and provide an effective domain generalization method for convolutional neural networks based on face padding. Also, we suggest a few methods of passing partial information about faces for training.
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Zainulin, R., Solovyev, D., Shnyrev, A., Isaev, M., Shipunov, T. (2023). Face Padding as a Domain Generalization for Face Anti-spoofing. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_5
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