Abstract
Face authentication and biometrics are becoming a commodity in many situations of our society. As its application becomes widespread, vulnerability to attacks becomes a challenge that needs to be tackled. In this paper, we propose a non-intrusive on the fly liveness detection system, based on 1D convolutional neural networks, that given pulse signals estimated through skin color variation from face videos, classify each signal as genuine or as an attack. We assess how fundamentally different approaches – sequence and non-sequence modelling – perform in detecting presentation attacks through liveness detection. For this, we leverage on the Temporal Convolutional Network (TCN) architecture, and exploit distinct and TCN grounded types of convolution and architectural design schemes. Experiments show that our TCN model provides the best balance in terms of usability and attack detection performance, achieving up to 90% AUC. We further verify that while our 1D-CNN with a residual block variant performs on par with the TCN model in detecting fake pulses, it underperforms in detecting genuine ones, leading to the conclusion that the TCN model is the most adequate for a production environment. The dataset will be made publicly available to foster research on the topic. (https://github.com/novasearch/Mobile-1-D-Face-Liveness-Detection)
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Padnevych, R., Carmo, D., Semedo, D., Magalhães, J. (2022). Temporal Convolutional Networks for Robust Face Liveness Detection. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_21
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