Biometric identification uses individuals' characteristics to attempt to match a sample to a database of existing samples. An increasingly commonly used characteristic is the iris section of the eye, which is valued for its uniqueness among individuals and stability over time. One key concern with iris recognition systems is the time required to find a test sample's match in a database of subjects. This work considers methods for categorizing irises within a database, so that a search for a match to a test sample can be focused on the test sample's category. The main method for categorization used in this work is texton learning. Texton learning involves creating a vocabulary of features and determining how much of each feature a given sample has. Once images are represented by textons, they are clustered in an unsupervised process. Success of the system is assessed as its ability to take a previously unseen image from a subject and classify it the same as the database reference for the subject. This work improves upon the past applications of texton learning with more thorough experiments to determine the optimal number of textons and image clusters. This system also investigates different accuracy metrics, with this work detailing two key methods and their relative benefits. Additionally, more in depth analysis is given for potential time saving impacts for finding a database match. Beyond the improvements to texton learning, symbolic features (ethnicity and gender) have been incorporated into the categorization process using a probabilistic metric. This is an innovative combination of using the numerical representation of the iris along with demographic information.