Abstract: 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.

Key words: deep learning, multibiometric systems, iris, palmprint, face, convolutional neural network.