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A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. There are many studies focusing on iris recognition. However, several negative results may occur of the iris images acquired under less-constrained environments. This work is to enhance the performance of the segmentation process in iris recognition systems to increase the overall accuracy. The iris recognition system consists of several stages after acquiring the image of the iris, segmentation, normalization, feature extraction, and matching. first, the segmentation stage includes steps several are pre-processing, truncated TV model, edge detection, determining the coordinates of the center and radius of the pupil and iris, noise removal, and eyelid detection. Second, the normalization stage used Daugman's Rubber Sheet model for normalization of iris images. Third, feature extraction utilizing Local Binary Pattern (LBP) and Chunked Encoding method, finally, performed the matching process using Hamming Distance.