Performance Evaluation of Different Distance Measures Used in Color Iris Authentication
نویسندگان
چکیده
This paper proposes performance evaluation of different distance measures used in color iris authentication. The color iris segmentation is carried out using histogram and circular Hough transform. The color iris features are extracted using histogram method. Different distance measures are used for iris authentication. The experimental evaluation shows that Euclidean and Manhattan distance are computationally efficient as compared to other distances. The proposed method gives very promising results which achieves classification accuracy of 92.1%. Equal error rate of 0.005 and 0.072 for Euclidean and Manhattan distance for HSV model and 0.09 and 0.098 for RGB model respectively.
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