With the increasing requirements for security, biometrics based personal identification methods have received extensive attention. Recently, iris recognition is becoming an active topic in biometrics because of its uniqueness, stability,un-intrusiveness and high anti-forgery features. With the rapid expansion of applications, the number of users is increasing and the size of the iris database is expanding, so it needs more time for one-to-many iris recognition. Iris classification will effectively reduce the search time, that will improve the overall performance of an iris recognition system. To the best of our knowledge, this is the first attempt on iris classification. The main contributions of our work reported in this thesis are as follows: 1. In order to better carry out iris recognition and iris classification study, we employ several iris sensors to establish the CASIA V3.0 iris image database. It is now the largest open and free iris database in the world. 2. A novel ethnic classification method based on the global texture information of iris images is proposed in this thesis. Experiment results provide scientic evidence for the biological relationship between iris patterns and genetic factors. 3. We show that iris pattern is a kind of phenotypic feature with relation to the genes from statistics. At a small scale, the local features of the iris are unique to each subject, whereas at a large scale, the global features of the iris are similar for a specific race, and they seem to be dependent on the genes. 4. A novel algorithm is proposed for iris classification based on texture analysis with Local Binary Patterns(LBP). 5. Based on multi-channel Gabor filtering and machine learning, a novel method is proposed to learn a small finite vocabulary of micro-structures, which are called Iris-Textons. Then Iris-Texton histogram is used as feature vectors of iris textures, which is successfully applied to iris classification and ethnic classification. 6. By applying iris classification in iris recognition systems, the iris recognition speed is improved greatly. In this thesis, we propose a concept of continuous iris classification, and combine iris classification with traditional iris recognition algorithm by a cascade classifier. We develop this method in embedded systems, which keep high accuracy of iris identification, but reduce the search time during the matching procedure.