Iris Recognition based on Pupil using Canny edge detection and K-Means Algorithm

In eye authentication process, the pupil detection is most crucial step to recognize the eye. In eye, iris and sclera are used as the previous inputs using to recognize the eye with different mechanisms like segmentation combining with different versions. The inner edge in the eye is not a normal circle, which may create problem in accurate recognition. In segmentation process, if the image is having less texture then it leads to iris legacy. In this paper, we concentrate only on pupil to recognize the eye. To find the edges in the image we propose canny edge detection method, to reduce the noisy data in the image and detecting the edges. After detecting edges, those images are stored in CASIA database. Secondly, the K- means method is to identify the nearest pupil edge images from the database for the given input image. The results show that identifying pupil is better method to recognizing the eye and raising the recognition accuracy.