Abstract:
Biometric is a system to identify the individual human by extracting distinguishable features from that particular person. Among many other biometric systems the iris recognition system is most accurate one right now since it has a high recognition rate. This thesis is proposing a system with four major division in the process: Segmentation, Normalization, Feature encoding and Matching. At early stage, Histogram equalization is used on the input image and to detect the objects present in the image, Canny Edge Detection model is employed. Inner circular boundary and center of the pupil in the Iris region is detected by using Hough Transformation. A circle is drawn with the help of Mid-Point Circle drawing algorithm which center is as same as pupil center and hence, outer circular boundary of the iris region can be detected. For the normalization, Daugman’s Rubber Sheet model is used and in the feature encoding process, instead of Gabor filter to extract feature from the iris image, Log-Gabor filter is used in this thesis since it has non-zero DC component advantage over Gabor filter. Last but not the least, Hamming Distance is used to compare two binary iris template for matching purpose.
