Face anti-spoofing methods based on physical technology and deep learning

Abstract

The mission of face anti-spoofing is to prevent facial fraud methods from creating security vulnerabilities in fraudulent systems and to improve system security and surveillance capabilities. With the widespread use of deep learning, face antispoofing methods have also seen a dramatic change. According to the chronological order of face anti-spoofing technology development and key technical challenges, this paper reviews face anti-spoofing algorithms from two aspects of traditional methods and deep learning-based methods. Firstly, based on extensive reading of the literature, this study analyzes traditional face anti-spoofing methods from the perspective of action command face live detection based on motion information/heuristic algorithm, face detection based on in vivo vital information and 3D face. Secondly, this paper further analyzes the deep learning-based face anti-spoofing method from the perspective of network structure and its variants of face anti-spoofing algorithms, face anti-spoofing with dual-stream training strategy, face anti-spoofing based on context feature and face anti-spoofing based on deep spatial and temporal information. Thirdly, the general data sets of face antispoofing are introduced, while the performance of representative algorithms in this field is compared and analyzed in detail. Finally, this article summarizes the existing problems and predicts future research directions in face anti-spoofing.