International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 5, October 2021, pp. 4037~4049 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i5.pp4037-4049  4037 Journal homepage: http://ijece.iaescore.com Occluded iris classification and segmentation using self- customized artificial intelligence models and iterative randomized Hough transform Isam Abu Qasmieh, Hiam Alquran, Ali Mohammad Alqudah Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan Article Info ABSTRACT Article history: Received Sep 26, 2020 Revised Mar 23, 2021 Accepted Apr 5, 2021 A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is cross- correlated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iris- code Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability. Keywords: Biometrics Iris segmentation Iterative randomized Hough transform Largest inscribed rectangle Normalized cross-correlation Occluded iris classification Self-customized SVM This is an open access article under the CC BY-SA license. Corresponding Author: Hiam Alquran Department of Biomedical Systems and Informatics Engineering Yarmouk University 566 Shafiq Irshidat Street, Irbid 21163, Jordan Email: heyam.q@yu.edu.jo 1. INTRODUCTION The security is a crucial issue; it needs precise and robust alternatives or support to password and personal identification number (PIN) because the computer hacking is greatly rising permanently [1]. Biometric technology treats such problems, because of an individual's biometric data uniqueness and cannot be converted nor transferred [2]. One of the most common techniques in biometric recognition systems is iris recognition [3]. It utilizes pattern recognition methods based on high-quality iris images [4]. The diversity of the iris texture is appropriate for using it in biometric systems; in addition to its intrinsic isolation and protection from the external environment [5]. The iris texture pattern is a phenotypic feature (genetically independent) and is stable over time [6], [7]. Several methods have been proposed to segment the iris and classify it from the non-iris region. Paul [8] extracted iris features using Gabor filter after utilizing Hough transform for iris region localizing and then feed the proposed features to Hamming distance classifier. Radu [9] designed multiple classifiers to enhance the iris recognition of noisy eye colored images by extracting the iris colored features. Kekre [10] used Haar