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.

Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification

“…This study uses the extraction of the Gray Level Co-occurrence Matrix (GLCM) feature and the K-Nearest Neighbor (KNN) classification in identifying butterfly images. The GLCM and KNN methods have been widely used in pattern recognition research, such as research Kulkarni et al [2] that recognizes human iris based on 3 types of models: Outer Circle Mask, Inner Circle Mask and ROI based Iris using GLCM texture features resulting in an accuracy rate of 96.3%. Kaushal and Bala [3] identified plant diseases based on GLCM texture features, K-Mean Clustering segmentation, and KNN classification producing an accuracy of 80% -90%, then research Indriani et al [4] in identifying maturity Tomato fruit based on GLCM texture features and HSV colour features resulting accuracy rate of 90% -100%.…”