Advanced Face and Liveness Detection in real-time using multi-model learning to combat Spoofing Attack

This paper presents an advanced approach to face and liveness detection in real-time, leveraging multi-model learning techniques to enhance security against spoofing attacks. With the rise of biometric authentication, the vulnerability of face recognition systems to spoofing—using photographs, videos, or masks—has become a significant concern. Our method integrates multiple machine learning models, including convolutional neural networks (CNNs) and Fisherface, to effectively analyze facial features and temporal dynamics. By utilizing diverse datasets and combining features such as texture, motion, and depth, the system improves accuracy in distinguishing between genuine users and fake user. Real-time processing capabilities are achieved through optimization techniques, ensuring swift and reliable detection. This research contributes to the development of robust security solutions in various applications, such as mobile banking, access control, and identity verification systems.