Face Spoofing Detection Based on Color Texture Markov Feature and Support Vector Machine Recursive Feature Elimination

Aiming to counterstrike face spoofing attacks such as photo attacks and video attacks, a face spoofing detection scheme based on color texture Markov feature (CTMF) and support vector machine recursive feature elimination (SVM-RFE) is proposed. In this paper, the adjacent facial pixels discrepancy between the real and the fake face is analyzed, and texture information between the color channels is fully considered. Firstly, the directional difference filter is used to capture the facial texture difference between the real and the fake face, which can be regarded as low-level features of CTMF. Then, the facial texture difference is modeled by the Markov process to form a high-level representation of the low-level features. Meanwhile, the mutual information of facial texture between the color channels, which is ignored in the previous literature, is investigated. In addition, SVM-RFE is utilized to reduce the feature dimension and makes it suitable for real-time detection. Experiments on four public benchmark databases indicate that the proposed scheme can effectively resist photo and video spoofing attacks in face recognition.