This paper presents a new human recognition method based on features extracted from retinal images. The\r\nproposed method is composed of some steps including feature extraction, phase correlation technique, and feature\r\nmatching for recognition. In the proposed method, Harris corner detector is used for feature extraction. Then,\r\nphase correlation technique is applied to estimate the rotation angle of head or eye movement in front of a retina\r\nfundus camera. Finally, a new similarity function is used to compute the similarity between features of different\r\nretina images. Experimental results on a database, including 480 retinal images obtained from 40 subjects of DRIVE\r\ndataset and 40 subjects from STARE dataset, demonstrated an average true recognition accuracy rate equal to\r\n100% for the proposed method. The success rate and number of images used in the proposed method show the\r\neffectiveness of the proposed method in comparison to the counterpart methods.
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