Face liveness detection: Fusing colour texture feature and deep feature

Abstract

The identification which uses biological characteristics has been a current top in the recent past. However, numerous spoofing skills occur with the rising prosperity of advance recognition technology, especially in the detection and recognition of a face. In allusion to the problem above, more robust and accurate face spoofing detection schemes have been put forward. Convolutional neural networks (CNNs) have demonstrated extraordinary success in face liveness detection recently. In this study, an effective face anti-spoofing detection method based on CNN and rotation invariant local binary patterns (RI-LBP) has been proposed. First, the authors use CNN to extract deep features and use RI-LBP to extract colour texture features. In addition, the principal component analysis approach is employed to decrease the dimensions of deep characteristic. Moreover, two different features are fused before applying to support vector machine (SVM). Finally, the SVM classifier is adopted to identify genuine faces from fake faces. They have conducted extensive experiments to obtain a scheme of better generalisation capability for face anti-spoofing detection. The analysis results indicate that the proposed approach implements great generalisation capability over other state-of-the-art approaches within the intra-databases and cross-databases.