In applications of cyberspace security, face liveness detection is significant for face recognition systems. In this paper, an innovative face anti-spoofing algorithm against video-based face spoofing attacks is proposed based on local binary patterns and multilayer discrete cosine transform (LBP-MDCT) and convolutional neural network (CNN). First, we first extract face images from a target video, generate LBP features for each extracted face image and perform multilayer DCT onto the features to obtain LBP-MDCT features. Second, we input part of face images into CNN to obtain CNN features. After that, the two types of features are respectively input into support vector machine (SVM) classifier. In the last stage, the SVM output is fused with a decision-level operation to determine whether the target video is a spoof attack or a valid access. Compared with existing algorithms, the experimental results on two benchmarking datasets (Replay-Attack dataset and CASIAFASD dataset) demonstrate the excellent effectiveness of the proposed method.
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