A Review on Facial Anti-spoofing Techniques

Face-based biometric technology finds extensive use in authentication applications due to the human face being readily accessible and containing abundant information in daily life. However, facial recognition systems face the challenge of thwarting face spoofing attacks. Numerous spoof detection systems have been introduced, each with distinct merits and limitations. Ensuring precision in face-based identity recognition and access control is imperative to counter potential threats. Notable face spoofing detection algorithms, including CNNs, SVMs, modified CNNs, and various classifier techniques, have demonstrated efficacy. Nonetheless, as face spoofing tactics have evolved from exploiting printed photos to incorporating masks, there arises a pressing need for performance enhancement, necessitating regular strategy updates. This work aims to provide a comprehensive overview of anti-spoofing techniques, with a specific focus on established and widely adopted face spoofing countermeasures. The present study examines the proposed methodologies and ongoing efforts in this domain, encompassing performance assessments conducted so far. This collective analysis serves to unearth novel avenues and refine existing methodologies, contributing to the continuous advancement of this rapidly expanding realm of research.