Effect of Tiling in Row Mean of Column Transformed Image as Feature Vector for Iris Recognition with Cosine, Hadamard, Fourier and Sine Transforms

Abstract

recognition is a biometric authentication method that uses pattern-recognition techniques based on high-resolution images of the irises of an individual's eyes. Iris recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of iris recognition algorithms have been developed for decades. This paper presents the techniques of iris recognition using image transforms such as Cosine transform, Sine transform, Fourier transform and Hadamard transform. Here iris recognition is done using the image feature vector set extracted as row mean of transformed column iris image. Image tiling is further used for feature extraction for each transform and the performance is compared with the single tile based iris recognition method. Parameters such as False Acceptance Rate and Genuine Acceptance Rate are used to test the performance of the techniques. The results have shown that the proposed Iris recognition methods performs better with increased number of tiles of Iris image up to certain extent of tiling.