With the recent emphasis given to security, automatic human identification has received significant attention. In particular, iris based subject recognition has become especially important because of its high level of complexity which lends itself to high confidence recognition. In addition, the eye is well protected and generally does not change very much over extended periods of time. This thesis gives a review of some currently available methods that have already been investigated. A wide sense stationary approximation for gray scale values is explored as a possible means of feature extraction. The singular value decomposition (SVD) is discussed as a low bit rate tool for iris discrimination. The 2D principal component analysis (2DPCA) is explored as a method for feature extraction. It is determined experimentally that the SVD for iris recognition is a novel way to significantly reduce the storage requirements (133 bits) for iris recognition as compared to other methods (2048 bits). However, recognition accuracy has not reached a desirable level. The 2DPCA, on the other hand, significantly improves recognition accuracy on the same dataset, but at the cost of greater storage requirements.