Enhancing biometric authentication through multimodal approach combining face and fingerprint recognition using convolutional neural networks (CNN)

A novel multimodal biometric authentication system combining face and fingerprint verification to ensure enhanced security, accuracy, and resilience in user identification, is presented in this work. The system utilizes Convolutional Neural Networks (CNNs) for effective feature extraction from both biometric modalities, addressing challenges such as occlusion, lighting, and finger quality in real-world scenarios. The results prove the performance of the system, with unimodal face authentication achieving 99.66% accuracy, unimodal fingerprint authentication reaching 100% accuracy, and the multimodal system is 98.35% accurate overall. The multimodal system offers improved security and robustness by significantly reducing the False Rejection Rate (FRR) and False Acceptance Rate (FAR), even though its accuracy is marginally lower than that of the unimodal systems. The combination of fingerprint and face modalities allows for improved performance by compensating for the weaknesses of individual modalities. The study highlights the potential of deep learning in biometric systems, providing a robust solution for secure access control in mobile and high-security applications. The system was evaluated using benchmark datasets, including the Georgia Tech face database, Essex face dataset, FVC2000, and SOCOFing dataset. Future work will focus on the further enhancement of the system, for real-time deployment on mobile devices, expanding the model’s applicability across diverse environments, and exploring the integration of additional biometric modalities.

Text 123950.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) Official URL or Download Paper: https://link.springer.com/article/10.1007/s10791-0...

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