Iris Feature Extraction Using Gray Level Co-occurrence Matrix and Gabor Kernel Filter
Rosa Andrie Asmara, Rizqi Darma Rusdiyan Yusron, Faisal Rahutomo, Rudy Ariyanto, Deddy Kusbianto Purwoko Aji, Priska Choirina

State Polytechnic of Malang

Abstract

Algorithms developed to identify people with iris image data have been tested in many field and laboratory experiment. This paper makes a parameter analysis of iris image used to recognize human. Iris recognition system, which is applied based on segmentation, normalization, encoding, and matching is also describe in this paper. Circle Hough Transform the segmentation module used to find the inner and outer boundaries of the iris. The experiment was carried out using CASIA v1 iris database with grayscale images. Shape, intensity, and location information for localizing the pupil or iris and normalizing the iris area a used iris segmentation the by unwrapping the circular area into a rectangular area. Normalized area will be used to extract the features using Gray Level Co-occurrence Matrix (GLCM) and Gabor filter, the feature compared the recognition accuracy using Support Vector Machines (SVM) and Naive Bayes classifiers. GLCM feature test results achieved 95.24% SVM classification accuracy, where as using achieved 85.71% Naive Bayes. Gabor feature test results achieved 95.24% SVM classification accuracy, where as using achieved 95.23% Naive Bayes. The classification process based on GLCM and Gabor features show that the SVM method have to highest recognition accuracy compare to Naive Bayes classifier.

Keywords: Gray Level Co-occurrence Matrix; Gabor Kernel Filter; Iris Feature; Feature Extraction

Topic: Computer Science

Link: https://ifory.id/abstract-plain/njQUMrgphTHe

Web Format | Corresponding Author (Rizqi Darma Rusdiyan Yusron)