Fine-tuned Siamese neural network–based multimodal vein biometric system with hybrid firefly–particle swarm optimization

Abstract

Recent advancements in biometric recognition focus on vein pattern–based person authentication systems. We present a multimodal biometric system using dorsal and finger vein images. By combining Siamese neural networks (SNNs) with hybrid firefly–particle swarm optimization (FF-PSO), we optimize finger and dorsal vein identification and classification. Using FF-PSO to tune SNN parameters is an innovative hybrid optimization approach designed to address the complexities of vein pattern recognition. The proposed system is tested with two public databases: the SDUMLA-HMT finger vein dataset and the Dr. Badawi hand vein dataset. The efficacy of the method is assessed using performance measures such as recall, accuracy, precision, F1 score, false acceptance rate, false rejection rate, and equal error rate. The experimental findings demonstrate that the proposed system achieves an accuracy of 99.5% with the fine-tune SNN and FF-PSO techniques and preprocessing module. The proposed system is also compared with various existing state-of-the-art techniques.