Robust User Authentication Using CNN-Based Face, Fingerprint, and Iris Biometrics

Abstract

User authentication plays an essential role in security assurance in the present-day digital world. Traditional password-based authentication approaches are increasingly becoming vulnerable to attacks, while single modal biometric systems suffer from challenges such as noise susceptibility, deception, and intra-class variations. This paper proposes a secure and reliable multimodal biometric system using fingerprints, facial characteristics, and irises using Convolutional Neural Network (CNN) to address these weaknesses. In this system, self-learning CNN based representations are utilized for every biometric characteristic, allowing for automatic extraction of distinct deep representations without hand-crafted features. The obtained confidence or attributes across different biometrics are merged to create a combined representation for authentication. Confidence-based classification approach is utilized for authenticating the genuine and impersonator user. Efficiency of the proposed system has been tested using unique assessment criteria including accuracy, recall, true negative rate, false positive rate, precision, and false negative rates. Experimental outcomes demonstrate that the presented CNN-based multimodal biometric system achieves superior accuracy and robustness compared to the conventional single-modal systems and thus it can be used for practical security applications where reliability is required.