SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept

Abstract

Face spoofing refers to “tricking” a facial recognition system to gain unauthorized access to a particular system. It is mostly used to steal data and money or spread malware. The malicious impersonation of oneself is a critical component of face spoofing to gain access to a system. It is observed in many identity theft cases, particularly in the financial sector. In 2015, Wen et al. presented experimental results for cutting-edge commercial off-the-shelf face recognition systems. These demonstrated the probability of fake face images being accepted as genuine. The probability could be as high as 70%. Despite this, the vulnerabilities of face recognition
systems to attacks were frequently overlooked. The Presentation Attack Detection (PAD) method that determines whether the source of a biometric sample is a live person or a fake representation is known as Liveness Detection. Algorithms are used to accomplish this by analyzing biometric sensor data for the determination of the authenticity of a source.