Affiliations: Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation, Russian | Tel.: +7 391 213 9623; Fax: +7 391 291 9147; E-mail: favorskaya@sibsau.ru
Correspondence:
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Corresponding author: Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation, Russian. Tel.: +7 391 213 9623; Fax: +7 391 291 9147; E-mail: favorskaya@sibsau.ru.
Abstract: The rapid development of biometric methods and their implementation in practice has led to the widespread attacks called spoofing, which are purely biometric vulnerabilities, but are not used in conjunction with other IT security solutions. Although biometric recognition as a branch of computer science dates back to the 1960s, attacks on biometric systems have become more sophisticated since the 2010s due to great advances in pattern recognition. It should be noted that face recognition is the most attractive topic for deceiving recognition systems. Popular presentation attacks, such as print, replay and mask attacks, have demonstrated a high security risk for SOTA face recognition systems. Many Presentation Attack Detection (PAD) methods (also known as face anti-spoofing methods or countermeasures) have been proposed that can automatically detect and mitigate such targeted attacks. The article presents a systematic survey in face anti-spoofing with prognostic trends in this research area. A brief description of 16 outstanding previous surveys on the face PAD field is mentioned, from which it is possible to trace how this scientific topic has developed. SOTA in PAD provides an analysis of a wide range of the PAD methods, which are categorized into two unbalanced groups: digital (feature-based) and physical (sensor-based) methods. Generalization of deep learning methods as a recent trend aimed at improving recognition results requires special attention. This survey presents five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods. A summary of over than 40 existing 2D/3D face spoofing databases is a guideline for those who want to select databases for experiments. One can also find a description of performance evaluation metrics and testing protocols. In addition, we discuss trends and perspectives in the emerging field of facial biometrics.
Keywords: Face biometric, face anti-spoofing, print attack, replay attack, 3D mask attack, plastic surgery attack, texture analysis, motion analysis, liveness detection, image quality analysis, database
