Iris Recognition Using Color and Texture Features

We approach the problem of iris recognition by combining both color and texture information. For color features, a well-known global color criterion was extended and for texture we adapted three classical methods: Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Gabor filters. As classifiers we employed k-NN (with Euclidean, Manhattan, Canberra and some variations of these distances), Support Vector Machines (SVM) and a two steps recognition process based on k-NN. We tested the methods using RGB, HSV, and LAB color spaces on two irises datasets (UBIRIS and UPOL). We get better results by combining color and texture features than by only considering color and texture separately.