  Facial biometrics has been recently received tremendous attention as a
convenient replacement for traditional authentication systems. Consequently,
detecting malicious attempts has found great significance, leading to extensive
studies in face anti-spoofing~(FAS),i.e., face presentation attack detection.
Deep feature learning and techniques, as opposed to hand-crafted features, have
promised a dramatic increase in the FAS systems' accuracy, tackling the key
challenges of materializing the real-world application of such systems. Hence,
a new research area dealing with the development of more generalized as well as
accurate models is increasingly attracting the attention of the research
community and industry. In this paper, we present a comprehensive survey on the
literature related to deep-feature-based FAS methods since 2017. To shed light
on this topic, a semantic taxonomy based on various features and learning
methodologies is represented. Further, we cover predominant public datasets for
FAS in chronological order, their evolutional progress, and the evaluation
criteria (both intra-dataset and inter-dataset). Finally, we discuss the open
research challenges and future directions.
