As the demand for efficient and secure identity verification increases, AI-driven biometric technologies continue to advance further. This study presents a cluster of biometric auth that employs AI with a novel approach, layering together two biometric technologies—facial recognition and fingerprint verification—so as to increase precision, and at the same time offering the user privacy and scalability. By performing score-level fusion at the backend, the system achieves more robust matching and significantly reduces false acceptance and rejection. For the data protection of the users, AES-256 encryption and SHA-256 hashing are the methods employed that give the users confidentiality and integrity at the same time. One of the unique features of the system is its capability of real-time dynamic enrollment, offline operation, and user-friendly interface developed in Python with OpenCV. Through the experimental implementation, the framework's potential is inferred to render identity verification that is not only efficient but also secure and thus suitable for sensitive environments. The present research demonstrates the practical benefits offered by the fusion of multimodal biometrics with AI in terms of strengthening authentication reliability without resorting to blockchain integration.
Ratnesh et al. (Wed,) studied this question.