Hybrid method for active face anti-spoofing based on close-up challenge

Facial authentication on mobile devices has become prevalent in various applications. Face Liveness Detection, or Face Anti-Spoofing (FAS), focuses on identifying attempts by malicious users to impersonate someone else or hide their own identity. One specific branch within this field is active liveness detection, which involves analyzing both the input signal and user behavior while they perform a required task to verify the authenticity of the presented face. Despite the significant amount of research in FAS, active liveness detection remains mostly underexplored. This gap has led to outdated methods, insufficient testing of proposed active techniques in diverse scenarios, and a lack of comparative analysis between different approaches. In this paper, we explore these differences by comparing the performance of the latest existing close-up methods with baseline models using ResNet-18 and ResNet-50. Furthermore, we introduce a new model that builds on previous work, combining projective invariants with facial embedding for robust feature extraction. This approach directly improves upon existing techniques, surpassing other baselines in detecting spoofing attempts.