A New IRISSegmentation Method BasedRepresentation on Sparse

Iris recognition is one of the most reliable methods for identification. Ingeneral, itmatching. Among them, iris segmentation has an important role on the performanceof any iris recognition system. Eyes nonlinear movement, occlusion, and specularreflection are main cpropose a new pupil localization method based on the sparse representation andsparse recovery (SR). The main advantage of our segmentation algorithm based onsparse representation in respect towhole image for iris region very fast. Also we have proposed a new method forenhancing the extracted iris template when the pupil boundary is noncircular, andalso a new method for creating occlusion mask bthresholding.with the same size dictionary and shown(PCA) with the SR classifier makes it very faster, whereas preserves thThe achieved results are evaluated with others in terms of the recognition accuracyand the segmentation time consuming where the CASIA Vconsists of image acquisition, iris segmentation, feature extraction andWe have compared the SR classifier and the Hamming distance (HD)hallenges for any iris segmentation method. In this paper, weother approaches is capability of searching thethat using theased on the histogramprincipal component analysis4 Lamp database usede accuracy..

A New IRISSegmentation Method BasedRepresentation on Sparse Keywords:

Iris Segmentation andDetection , Principal Component AnalysisRecognition , Sparse Representation , .Eyelid andEyelash