Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (7): 1195-1206.DOI: 10.3778/j.issn.1673-9418.2012010

• Surveys and Frontiers • Previous Articles     Next Articles

Review of Presentation Attack Detection in Face Recognition System

MA Yukun, XU Yaowen, ZHAO Xin, XU Tao, WANG Zerui   

  1. 1. School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang, Henan 453003, China
    2. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    3. School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, Henan 453003, China
  • Online:2021-07-01 Published:2021-07-09



  1. 1. 河南科技学院 人工智能学院,河南 新乡 453003
    2. 北京工业大学 信息学部,北京 100124
    3. 河南科技学院 信息工程学院,河南 新乡 453003


As the rapid development of face recognition technologies, face presentation attack detection (face liveness detection) techniques are facing new requirements, including instantaneity of?detection, generalization in complicated environments, robustness against various attack types, friendliness of user experience and etc. In?this?paper, the necessity of face presentation attack detection is explained, and the methods are classified, compared and summarized. In general, they can be divided as methods based on manual features and methods based on deep learning. Furthermore, the recent researches on algorithm generalization are summarized as the following categories: methods based on auxiliary supervision, methods based on domain adaptive or domain generalization, methods based on disentangled representation, methods based on noise modeling, and methods based on anomaly detection. Representative algorithm of each type of method are analyzed, and the basic ideas of each method are summarized in detail, as well as the merit and demerit. Issues surrounding face presentation attack detections are systematically summarized from different perspectives, including different types of presentation attacks, advanced detection methods, popular public databases, the standardized evaluation indicators, and testing method. In addition, the difficulties and challenges in this field are also discussed, and the future research direction and development trend are summarized.

Key words: face recognition, presentation attack detection, deep learning, texture?features



关键词: 人脸识别, 活体检测, 深度学习, 纹理特征