Using machine learning principles, the classification method for face spoof detection in artificial neural networks

The usage of biometric technologies for person authentication and verification has grown significantly, and these systems are now widely employed. This biometrics is essential to personal, governmental, and international security. This paper's major strategy is to explain the value of biometric systems. These days, various biometric system types are in use. These biometric technologies include facial recognition, voice recognition, palm vein biometrics, and iris recognition. Spoofing is the fundamental danger to all of these biometric systems. When compared to other biometric systems, the facial recognition biometric system is more commonly employed. Face-spoofing techniques include mask attacks, photographic and video attacks, and We examine various techniques for spotting Face spoofing. Face has recently received more attention in a wide range of fields because to its security and convenience. Biometric system-based face detection frequently employed for authentication of applications where human faces are the recognition of daily life as well as retaining of sensitive information. However, face spoofing assaults continue to pose a threat to face recognition systems. Although several face spoofing detection methods have been proposed by researchers and have demonstrated excellent performance, our goal is to create a system for face spoofing using machine learning algorithms.