Authors - Mannem Sri Nishma, Satendra Gupta, Tapas Saini, Harshada Suryawanshi, Anoop Kumar Abstract - Face recognition-based authentication has become a critical component in today's digital landscape, particularly as most business activities transition to online platforms. This is especially evident in the finance and banking sectors, which have shown significant interest in adopting online processes. By leveraging this technology, these industries can enhance operational efficiency, promote business growth, reduce reliance on manpower, and automate several processes effectively. However, face recognition systems are susceptible to face spoofing attacks, where malicious actors can attempt to deceive these systems using facial images or videos. Some attackers even use masks resembling authorized individuals to trick recognition cameras into perceiving them as real users. To counter such threats, liveness detection has emerged as a critical research area, focusing on identifying and preventing face spoofing attempts. The proposed approach utilizes a deep learning technique tailored for face liveness detection. The experiments are conducted using the Replay-Mobile, MSU-MFSD, Casia-FASD and our own datasets, which are widely used for recognizing live and spoofed faces. The system achieved an impressive area under the ROC curve (AUC) of 0.99, demonstrating its effectiveness in detecting face spoofing.
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