The rich texture of the iris can be used as a biometric cue for person recognition. The richness and variability observed in the iris texture is due to the agglomeration of multiple anatomical entities composing its structure. Due to the presence of distinctive information at multiple scales, a wavelet-based signal processing approach is commonly used to extract features from the iris. One of the most popular approaches to iris recognition generates a binary code to represent and match pairs of irides. First, a segmentation routine is used to detect the iris region in the ocular image captured by the camera. Next, a geometric normalization method is used to transform the nearly annular iris region into a rectangular entity. Then, this rectangular entity is convolved with a Gabor filter resulting in a complex response, and the phase information of the ensuing response is quantized into a binary code, commonly referred to as the iris code. Finally, Hamming distance is used to compare two iris codes and generate a match score, which is used for biometric recognition. This chapter discusses the salient aspects of a typical iris recognition system.