Global LBP features for Iris Recognition using blood vessel segmentation

Iris Recognition is found to be one of the most reliable and efficient technique for biometrics identification. In this paper iris recognition using blood vessel segmentation is proposed. After blood vessel segmentation, the segmented iris image is recognized using global texture features. The GLCM, Gabor and Local Binary Patterns are used for feature extraction. The for training of the extracted features using well known SVM classifier. The performance of the system is evaluated on DRIVE and High Resolution Image Databases. The system performs better for the combined features of GLCM and Gabor as compared to individually. The results of LBP are found to be more promising. This proposed approach of iris recognition using blood vessel segmentation is robust and secure and has the ability to recognize retinal images from the photographs of the known iris images. The system is more efficient in terms of accuracy as well as time complexity.