Feature Matching in Iris Recognition System using MATLAB
How to cite (IJASEIT) :
Naguru, Imran, and Narendra Kumar Rao B. “Feature Matching in Iris Recognition System Using MATLAB”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, Oct. 2017, pp. 1748-57, doi:10.18517/ijaseit.7.5.2765.
Citation Format :
Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques. In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
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