Deep Learning Approach based on Iris, Face, and Palmprint Fusion for Multimodal Biometric Recognition System
Khatri Manvi, Sharma Ajay · International Journal of Performability Engineering · 2023
With rising concerns about data theft and stricter security rules in many countries, biometric technology is becoming increasingly integral to our everyday lives.Given the severe limits of current single-modal biometric systems, it is no surprise that multimodal biometric approaches are experiencing a surge in popularity.Based on these findings, this work provides a novel multimodal biometric person identification system that combines iris, face, and palmprint biometric modalities for human recognition via the use of deep learning algorithms.The network relies on a convolutional neural network (CNN) to extract features, and a SoftMax and Tanh classifier to label images.The Adam and Adadelta optimisation technique are utilised to construct the CNN model, and the categorical cross-entropy loss function was implemented.As a result, the functional and evaluation levels were fused together.Several tests were conducted on the PolyU-IITD, PolyU Cross-Spectral Iris Image, and Tufts Face datasets to empirically evaluate the performance of the proposed system.In a biometric identification system, employing three biometric characteristics was shown to be superior to using one or two biometric features.Furthermore, the findings demonstrate that the proposed method achieves 100% accuracy, much better than existing state-of-the-art approaches.