This article proposed a novel human identification method based on retinal images. The proposed system\r\ncomposed of two main parts, feature extraction component and decision-making component. In feature extraction\r\ncomponent, first blood vessels extracted and then they have been thinned by a morphological algorithm. Then,\r\ntwo feature vectors are constructed for each image, by utilizing angular and radial partitioning. In previous studies,\r\nManhattan distance has been used as similarity measure between images. In this article, a fuzzy system with\r\nManhattan distances of two feature vectors as input and similarity measure as output has been added to decisionmaking\r\ncomponent. Simulations show that this system is about 99.75% accurate which make it superior to a great\r\nextent versus previous studies. In addition to high accuracy rate, rotation invariance and low computational\r\noverhead are other advantages of the proposed systems that make it ideal for real-time systems.
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