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Currently a lot of biometric procedures are being developed based on different features and algorithms. Nevertheless, it is known that, from all of these techniques, iris recognition is one of the most promising for high security applications. In this paper, a novel scheme is proposed to iris segmentation and recognition in iris based biometric system. In the new scheme, we use the modified circular fuzzy segmentor (MCFS) model to segment the pupil and iris inner boundary. After that, a binary encoder based feature extraction scheme named as LCE is proposed to extract the significant features to do the iris recognition process. Once feature extraction scheme is done by the LCE operator, the iris recognition is done through fuzzy logic classifier. We use three datasets from widely used iris databases (CASIA, MMU and UBIRIS) to analyze the increase of the error rates when the iris is inaccurately segmented. We selected 780 images of the CASIA, MMU and UBIRIS databases that the used segmentation algorithm can accurately segment. From the experimentation results, the proposed method of MCFS+LCE is outperformed than the existing methods.

Modified Circular Fuzzy Segmentor and Local Circular Encoder to Iris Segmentation and Recognition