1. Iris recognition using blood vessels segmentation
Authors : Kanchan S. Bhagat, Dr. Pramod B Patil
DOI : http://dx.doi.org/10.21172/1.111.04
Keywords : GLCM Gabor Local Binary Pattern Blood Vessel Segmentation SVMKNN Naïve Bayes. Abstract :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. For iris recognition well known SVM,KNN and Naïve Bayes classifiers are used.. The performance of the system is evaluated on DRIVE, DRIONS, High Resolution Image Database and real time image databases. The system performs better for the combined features of GLCM, LBP 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.
Citing this Journal Article :Kanchan S. Bhagat, Dr. Pramod B Patil, "Iris recognition using blood vessels segmentation", Volume 11 Issue 1 - July 2018,
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