Review of iris segmentation and recognition using deep learning to improve biometric application

Abstract

Biometric recognition is essential for identifying people in security, surveillance, and mobile device authentication. Iris recognition (IR) biometrics is exact because it uses unique iris patterns to identify individuals. Iris segmentation, which isolates the iris from the rest of the ocular image, determines iris identification accuracy. The main problem is concerned with selecting the best deep learning (DL) algorithm to classify and estimate biometric iris biometric iris. This study proposed a comprehensive review of DL-based methods to improve biometric iris segmentation and recognition. It also evaluates reliability, specificity, memory, and F-score. It was reviewed with iris image analysis, edge detection, and classification literature. DL improves iris segmentation and identification in biometric authentication, especially when combined with additional biometric modalities like fingerprint fusion. Besides, that DL in iris detection requires large training datasets and is challenging to use with noisy or low-quality photos. In addition, it examines DL for iris segmentation and identification efforts to improve biometric application understanding. It also suggests ways to improve precision and reliability. DL may be used in biometric identification; however, further study is needed to overcome current limits and improve IR processes.