Design and Development of Multimodal Biometric System Using Finger Veins and Iris by CNN Integrated with Hybrid SIO and Whale Optimization Techniques

In this study, we utilized an optimized Convolutional Neural Network (CNN) for multimodal biometric recognition. For optimization, a hybrid of Swarm Intelligence (SI) and Whale Optimization (WO) algorithms was employed. The Finger Vein (FV) and iris modalities were chosen for biometric recognition. Data for both modalities were collected from the SDUMLA-HMT database and preprocessed before being fed into the CNN model for feature extraction and selection. Following the CNN modeling, both feature- and score-level fusion techniques were applied for individual recog?nition. The developed hybrid SI-WO-CNN model was evaluated against two other optimized models, namely the SI-CNN and WO-CNN. Experimental results show that the proposed hybrid CNN model achieves the highest accuracy, reaching 99% through the score-level fusion technique. Furthermore, the proposed model was compared with recent research works, demonstrating its effectiveness in biometric recognition