Self mutated hybrid wavelet transform based iris recognition technique using partial energies of transformed iris images with cosine, walsh, sine and Kekre transform

In Iris recognition, the identification and authentication of an individual is carried out by analysis of unique patterns of iris. This paper presents, extracting the unique features from the iris images. The feature extraction is done by using concept of energy compaction. Palacky University Database is used as a test bed for proposed iris recognition technique, which contains 384 iris images of 64 persons, with 3 images for left eye and 3 images for right eye of each person. For the proposed iris recognition technique, self mutated hybrid wavelet transform of Cosine-Walsh, Cosine-Kekre and Cosine-Sine are used to generate transformed iris images. Considering all the coefficients for 100% of energy, feature vector of transformed iris image is generated, thus the size of feature vector becomes extremely large. For proposed Iris recognition technique, 99%, 98%, 97% and 96% of partial energies are considered, thus the number of coefficients considered to generate the feature vector are very less and the size of feature vector reduces severely. Accuracy using partial energies is high as compared to 100% of energy. The self mutated hybrid wavelet transform gives improvement in Genuine Acceptance Rate (GAR) and faster recognition. The best result is obtained for Cosine-Walsh self mutated hybrid wavelet transform as compared to other combinations.