GLCM-Based Multiclass Iris Recognition Using FKNN and KNN

Abstract

Iris recognition is one of the important authentication mechanism used extensively in biometric applications. The majority of the applications use single class iris recognition with normalized iris image. The proposed technique uses multi class iris recognition with region of interest (ROI) iris image on supervised learning. In this paper, the term ROI is referred as Un-normalized iris. The iris features are extracted using gray level co-occurrence matrix (GLCM) and a multiclass training vector is created. Further, iris image is classified based on fuzzy K-nearest neighbor (FKNN) and KNN classification. Test samples features are matched with the stored repository by various matching techniques such as max fuzzy vote, Euclidean distance, cosine and cityblock. The experiment is carried on standard database CASIA-IrisV3-Interval and result shows that multiclass approach with ROI segmented iris has better recognition accuracy using FKNN and KNN.