Iris recognition is an effective and widely used biotechnology, which has higher security performance than face recognition and fingerprint recognition. However, the overall performance of the recognition system is largely affected by the iris segmentation accuracy. In order to effectively improve the iris segmentation accuracy, based on the analysis of the physiological structure of iris, the literature in the relevant fields at home and abroad was reviewed, and the advantages and disadvantages of various algorithms were analyzed. A new accurate iris segmentation algorithm was proposed, which overcomes the hypothesis of concentric circles of traditional segmentation algorithms. Drawing on the idea of completed local binary patterns (CLBP) algorithm and fusing the grayscale information and structural information of the image, the shape-sensitive detection operator was proposed to effectively eliminate the two major factors that affect segmentation accuracy, i.e., the interference of eyelid and eyelashes. In addition, a segmentation process was proposed, which is divided into two parts: coarse iris segmentation and precise segmentation. Coarse segmentation includes outer contour and pupil rejection, and precise segmentation includes eyelid and eyelash rejection. Finally, a series of comparative tests were conducted to investigate accuracy and calculation efficiency on the iris datasets CASIA-IrisV3-Interval and CASIA-IrisV1 published by the Institute of Automation, Chinese Academy of Sciences. After using the proposed segmentation algorithm, the accuracy on the OSIRIS Version 4.1 iris recognition system reached 97.14% and 98.28% respectively, and the running time was significantly reduced, up to 0.699 s and 0.758 s respectively.