Comparative analysis of iris segmentation methods along with quality enhancement

Increased need of the automatic authentication of persons has led to extensive researches in biometrics. Among all biometrics, iris recognition is one of the most promising methods due to rich and unique textures of the iris, noninvasiveness, stability of iris pattern throughout the human life time, public acceptance, and availability of user friendly capturing devices. Iris segmentation is the vital step in iris recognition systems because all subsequent steps depend highly on its precision. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented accurately which will unavoidably result in poor recognition performance. A robust method for iris segmentation should be used to remove the influence of the noises as much as possible. In this paper, we present accuracy based comparative analysis of the three different methods for iris segmentation viz. Geodesic Active Contours (GACs), traditional Integrodifferential operator and Hough transform. Along with accurate segmentation the quality enhancement of encoded template is done by employing super resolution based on sparse signal representation approach. By directly super-resolving only the features essential for recognition, obtained from accurately segmented irises, recognition performance improvement is achieved. CASIA Interval version3 dataset is used for the experimentation in MATLAB based implementation.