Comparison between SVM and KNN classifiers for iris recognition using a new unsupervised neural approach in segmentation

A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristics he or she possesses. Iris recognition is a biometric identification method that applies pattern recognition to images of the iris. Owing to the unique epigenetic patterns of the iris, iris recognition is considered one of the most accurate methods in the field of biometric identification. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris. Then the normalization allows transforming the segmented circular iris region into a fixed-size rectangular shape using Daugman’s rubber sheet model. A discrete wavelet transformation (DWT) is applied to the normalized iris to lower the size of iris models and to improve classifier accuracy. Finally, the URIBIS iris database is used for individual user verification by using the KNN classifier or support vector machine (SVM) which based on the analysis of iris code as feature extraction is discussed