Empowering Biometric Security: A Paradigm Shift in Remote Identification
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Context: In an increasingly digital world, the need for secure and reliable identity verification is more critical than ever. Biometric authentication stands out due to its inherent uniqueness and resistance to forgery, yet its performance is still affected by real-world challenges such as image noise and lighting inconsistencies. Objective: This paper aims to enhance the robustness and accuracy of biometric systems, specifically for remote authentication scenarios, by addressing the limitations posed by environmental variations. Method: The proposed solution is a novel FKP-based biometric authentication system that employs a Triple Texture Feature Extraction (TTFE) technique to capture detailed information from both spatial and frequency domains. To further improve recognition performance, Projective Dictionary Learning is used to refine the feature representation. For secure data handling, a fuzzy vault scheme is integrated to encrypt biometric templates using secret keys, allowing secure authentication over potentially untrusted networks. Results: Experiments conducted on standard benchmark datasets demonstrate significant improvements in both accuracy and resilience to challenging conditions such as noise and lighting variations. Conclusions: This integrated approach successfully enhances the reliability and security of biometric authentication systems, paving the way for more practical and scalable real-world deployments.
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