A novel approach to iris recognition at-a-distance: leveraging BW-CNN framework

Abstract

A novel approach for iris recognition by integrating the Black Widow Optimization (BWO) algorithm with Convolutional Neural Networks (CNNs), forming the innovative Black Widow-CNN (BW-CNN) framework, is presented in this paper. The expanding field of biometrics has ushered in a transformative era of personal identification technologies, with iris recognition standing at the forefront due to its unparalleled security and reliability. The BW-CNN methodology is distinguished by its preprocessing steps, which include image normalization and segmentation, feature extraction through the ScaT-LOOP method, and optimized classification using CNNs fine-tuned with the BWO algorithm. This approach ensures high precision in iris recognition, making it suitable for various applications, from national security to public health. Experimental results, evaluated using comprehensive metrics such as sensitivity, specificity, accuracy, precision, F-measure, and others, demonstrate the superiority of the BW-CNN framework over existing methods. This research not only advances the capabilities of iris recognition systems but also contributes to the broader narrative of innovation in digital identity management, promising more secure and reliable biometric identification systems.