With the rapid development of information technology in society, personal authentication is becoming necessary in more and more fields. The traditional personal authentication methods cannot keep up with the requirements of the society because of their inherent defects. Under such circumstance, biometrics emerged as the time requires.As an important composing of biometrics, iris recognition is the most stable and reliable biometric technology. Compared with other biometric features, iris contains more distinctive information. Through analyzing the iris feature and combining with different feature extraction methods, we can realize iris recognition.As an organ of human body, iris plays an important role not only in identity identification, but also in medical diagnosis. In the west, iridology has a history more than one hundred years, and it has become a more completed system. Iridology is the study of the patterns and markings in the iris of the eye. Through analyzing the changes of the texture and the color of the iris, such as the crypt, the lacunae, the spots and lines, person can know if he is healthy.Based on the different applications of iris recognition and iris diagnosis, this paper studies the iris feature from textural pattern and structural pattern. The main research includes following parts:1. Owing to the shortcoming of the parameter accumulation operation and the slow operation speed of traditional iris localization algorithms, this dissertation investigates a fast algorithm for iris localization based on the detection of the longest chord. The proposed method accurately and fleetly locates the inner boundary of the iris by means of detecting the diameter of the pupil in the binary image. Then, the outer boundary is detected by a deformable circular template. The iris normalization is invariant for translation, rotation and scale after mapping into polar coordinates. Experimental results show this method has less computation and faster speed compared with other methods, the precision has achieved the pixels level and obtained the higher localization success rate.