Face Liveliness Detection Based on Texture and Color Features
Li Song, Hongbin Ma · 2019
Nowadays, face recognition has been used in many security occasions, but few of them have ability to distinguish real and fake faces. Besides, many researches on face liveliness detection mainly focused on intrusive methods, which are not user-friendly in practice. This paper proposes a novel non-intrusive face liveliness detection method based on the analysis of texture and color features. More specifically, this method adopts an improved local ternary pattern (LTP) to classify the nearby pixels. Based on the face pixel analysis, the infinity norm of pixel matrices is added as new features. The effectiveness of feature selection has been validated by different kinds of experiments on three challenging face anti-spoofing databases (NUAA, CASIA FASD and Replay-attack). This method reaches a compromise between number of features and accuracy, which means it also works on embedded systems.