Abstract
Face recognition systems are gaining momentum with current developments in computer vision. At the same time, tactics to mislead these systems are getting more complex, and counter-measure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transfer learning using a pre-trained CNN model using only static features to recognize photo, video or mask attacks. We tested our approach on the REPLAY-ATTACK and 3DMAD public databases. On the REPLAY-ATTACK database our accuracy was 99.04% and the half total error rate (HTER) of 1.20%. For the 3DMAD, our accuracy was of 100.00% and HTER 0.00%. Our results are comparable to the state-of-the-art.
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Lucena, O., Junior, A., Moia, V., Souza, R., Valle, E., Lotufo, R. (2017). Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_4
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DOI: https://doi.org/10.1007/978-3-319-59876-5_4
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