Performance Evaluation of A Three-Modality Biometric System using Multinomial Regression - Bopatriciat Boluma Mangata; TrÃ©sor Mazambi Kilongo; Pierre Tshibanda wa Tshibanda; Remy Mutapay Tshimona; Jean Pepe Buanga Mapetu; EugÃ¨ne Mbuyi Mukendi (Journal of Innovation Information Technology and Application (JINITA), 2025)
In this article, we explored key concepts related to technology and system efficiency. We have created an innovative biometric system that combines three modalities: fingerprint, facial recognition and voice recognition. This approach guarantees enhanced security and a seamless user experience for access control. We tested our application to obtain the false rejection rate and the false acceptance rate, which gave us the confusion matrix. We then used the multinomial regression method to obtain the various parameter values, which are: FN=0.124, VPP=0.88, Sp=0.88, VPN=0.87, Se=0.87 and F-measure = 0.87 for voice recognition, FN=0.104, VPP=0.90, Sp=0.90, VPN=0.89, Se=0.89 and F-measure = 0.89 for face recognition, FN=0.08, VPP=0. 92, Sp=0.92, VPN=0.91, Se=0.91 and F-measure = 0.91 for fingerprints and FN=0.004, VPP=0.99, Sp=0.99, VPN=0.99, Se=0.99 and F-measure = 0.99 for the global system resulting from the fusion of these three modalities. From this result, we can say that using the global fusion of these three modalities, our system is very efficient compared to separate systems which give an advantage to the fingerprint recognition system followed by facial recognition and finally voice recognition. We recommend further studies to evaluate the performance of our system in real scenarios, using methods such as multinomial regression. This work paves the way for significant advances in the field of biometric systems and methods such as multinomial regression. We hope that these results will inspire further research and practical applications for a connected and secure world.