Abstract
Facial biometric systems are extensively applied in diverse sectors for the purposes of person authentication and verification, primarily due to the distinctive nature of individual facial characteristics. Deep learning models are typically used in face authentication to validate people with excellent recognition accuracy. However, these systems are susceptible to a variety of cyber attacks that manipulate the digital representations of real-world faces to cheat the models. In the contemporary landscape of digital identity theft, liveness detection stands as a crucial technology. The need for enhanced security prompts the demand for a resilient system that can effectively counter face spoofing attempts and prevent unauthorized access. A Binary Authentication Protocol (BAP) technique is proposed to enhance facial biometric security in combination with Visual Speech Recognition (VSR). In the proposed method, the first verification step entails face authentication. Further, the authentication protocol involves a challenge-response-based method using VSR. The proposed method achieved a word error rate of 2.7% and a word recognition rate of 97.3%, surpassing existing state-of-the art methods in VSR. The proposed scheme offers practical and effective solutions to prevent face spoofing through active liveness detection in face-based authentication systems.
