Optimization driven deep belief network using chronological monarch butterfly optimization for iris recognition at-a-distance

Abstract

Identification of eye considering biometric traits is an essential field to recognize persons. Biometrics using iris images seems to be an effective identification of individuals. Various Iris Recognition at-Distance (IAAD) systems are used for extracting features of iris and improve image quality using the biometric model. Even though the quality of the iris is better, accuracy is a challenging question for the research community. Thus, an effective IAAD, namely Chronological Monarch Butterfly Optimization-Deep Belief Network (Chronological MBO-DBN) is devised to detect iris. The detection of iris using DBN is trained with Chronological MBO, which is the integration of Chronological theory and Monarch Butterfly Optimization (MBO). The features of iris are extracted with ScatT-Loop descriptor and Local Gradient Pattern (LGP) and subjected to Chronological MBO-DBN for the recognition of iris which improved accuracy. The implementation of proposed Chronological MBO-based DBN is performed using the dataset, CASIA Iris, and efficiency is evaluated by the accuracy of 96.078%, False Rejection Rate (FRR) of 0.4745% False Acceptance Rate (FAR) of 0.4847%, and F-Measure of 98.658%.