An effective model for the iris regional characteristics and classification using deep learning alex network

Abstract

Iris biometrics is one of the fastest-growing technologies, and it has received a lot of attention from the community. Iris-biometric-based human recognition does not require contact with the human body. Iris is a combination of crypts, wolflin nodules, concentrated furrows, and pigment spots. The existing methods feed the eye image into deep learning network which result in improper iris features and certainly reduce the accuracy. This research study proposes a model to feed preprocessed accurate iris boundary into Alexnet deep learning neural network-based system for classification. The pupil centre and boundary are initially recorded and identified from the given eye images. The iris boundary and the centre are then compared for the identification using the reference pupil centre and boundary. The iris portion, exclusive feature of the pupil area is segmented using the parameters of multiple left-right point (MLRP) algorithms. The Alexnet deep learning multilayer networks 23, 24, and 25 are replaced according to the segmented iris classes. The remaining Alexnet layers are trained using the square gradient decay factor (GDF) in accordance with the iris features. The trained Alexnet iris is validated using suitable classes. The proposed system classifies the iris with an accuracy of 99.1%. The sensitivity, specificity, and F1-score of the proposed system are 99.68%, 98.36%, and 0.995. The experimental results show that the proposed model has advantages over current models.