Performance Evaluation of Face Anti-Spoofing Method Using Deep Metric Learning from a Few Frames of Face Video
Koichi Ito, Asateru Kimura, Takafumi Aoki · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference · 2020
Recent advances in face recognition and deep learn-ing technologies are enabling us to identify individuals from images captured by a camera from a distance. On the other hand, there is a problem that a malicious person can impersonate the registered user by presenting a photo or video of the registered user’s face. Spoofing detection using video input, from which more features can be extracted than images, has not been studied very much. In this paper, we propose a method for detecting spoofing from video images of a small number of frames. The proposed method uses features extracted from video images using 3D Convolutional Neural Network (3D CNN). We also use deep metric learning to improve the accuracy of detection. We demonstrate the effectiveness of the proposed method through performance evaluation experiments using a large-scale spoofing attack dataset.