A Transfer Learning Approach for Face Liveness Detection

In the rapidly advancing domain of digital security, the authenticity of facial recognition systems remains essential. Face spoofing attacks pose a significant challenge, with malicious entities attempting to deceive facial authentication systems using counterfeit techniques. In this study, we present a novel transfer learning methodology leveraging the MoViNet model, renowned for its efficiency in video data processing, to discern genuine from spoofed faces. Our approach addresses the specific challenges of face liveness detection by fine-tuning the MoViNet model on a diverse dataset of both genuine and counterfeit face videos, enhanced by preprocessing steps such as frame extraction, padding, resizing, and normalization. This meticulous adaptation not only improves training but, through our transfer learning technique, equips the model with a robust defense against new and unseen spoofing attempts, ultimately achieving a notable zero False Acceptance Rate (FAR) – a testament to its high generalizability and uncompromising efficacy against spoofing. Post-training, our MoViNet-based model exhibited a remarkable accuracy of 98%, with a Precision of 1.0, an F1-Score of 0.94, and most notably, an Equal Error Rate (EER) and Half Total Error Rate (HTER) of 0.05%. When compared with several state-of-the-art methods, our approach not only matched but surpassed their efficacy. The findings underscore the potential of MoViNet in fortifying facial recognition systems against spoofing attacks, setting a new benchmark in the field, and offering robust protection for various real-world applications. We believe our transfer learning approach opens exciting possibilities for enhancing security and user experience in diverse domains.