A computer-implemented method for implementing face spoofing detection using a physical-cue-guided multi-source multi-channel framework includes receiving (410) a set of data including face recognition data, liveness data and material data associated with at least one face image, obtaining (420) a shared feature from the set of data using a backbone neural network structure, performing (430), based on the shared feature, a pretext task corresponding to face recognition, a first proxy task corresponding to depth estimation, a liveness detection task, and a second proxy task corresponding to material prediction, and aggregating (440) outputs of the pretext task, the first proxy task, the liveness detection task and the second proxy task using an attention mechanism to boost face spoofing detection performance.
FACE SPOOFING DETECTION USING A PHYSICAL-CUE-GUIDED MULTI-SOURCE MULTI-CHANNEL FRAMEWORK