Face Anti-spoofing: A Comparative Review and Prospects
Wonjun Kim · IEIE Transactions on Smart Processing and Computing · 2021
Face recognition and verification have been widely used for user authentication on various devices. To protect personal biometric information, face anti-spoofing techniques have been explored based on learning of the properties of various spoofing attacks against real faces. However, the direction of these studies has now encountered an important issue of validation for unseen types of spoofing attacks. Most methods have focused on revealing the differences between fake and real faces in a given dataset, but the performance significantly drops when such methods are tested on unseen samples that are different from those of the dataset used for training models. To cope with this limitation, several researchers have started to generalize the feature space across different datasets (i.e., free to the domain property). The goal of this paper is to provide a comprehensive review of face anti-spoofing methods with a systematic taxonomy, methodologies, and constructive prospects.