Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

The widespread deployment of facial recognition-based biometric systems has made facial
presentation attack detection (face anti-spoofing) an increasingly critical issue. This survey …

Face anti-spoofing is crucial to prevent face recognition systems from a security breach.
Previous deep learning approaches formulate face anti-spoofing as a binary classification …

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in
securing face recognition systems from presentation attacks (PAs). As more and more …

The face image is the most accessible biometric modality which is used for highly accurate
face recognition systems, while it is vulnerable to many different types of presentation …

Existing domain generalization methods for face anti-spoofing endeavor to extract common
differentiation features to improve the generalization. However, due to large distribution …

Many prior face anti-spoofing works develop discriminative models for recognizing the
subtle differences between live and spoof faces. Those approaches often regard the image …

Face anti-spoofing is critical to the security of face recognition systems. Depth supervised
learning has been proven as one of the most effective methods for face anti-spoofing …

Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces
as the genuine users. While advanced face anti-spoofing methods are developed, new …

Face anti-spoofing is crucial to security of face recognition systems. Previous approaches
focus on develo** discriminative models based on the features extracted from images …