This document presents the work carried out for the purposes of completing the Engineering Doctorate (EngD) program at the Institute for System Level Integration (iSLI), which was a partnership between the universities of Edinburgh, Glasgow, Heriot-Watt and Strathclyde. The EngD is normally undertaken with an industrial sponsor, but due to a set of unforeseen circumstances this was not the case for this work. However, the work was still undertaken to the same standards as would be expected by an industrial sponsor. An individual’s biometrics include fingerprints, palm-prints, retinal, iris and speech patterns. Even the way people move and sign their name has been shown to be uniquely associated with that individual. This work focuses on the recognition of an individual’s iris patterns. The results reported in the literature are often presented in such a manner that direct comparison between methods is difficult. There is also minimal code resource and no tool available to help simplify the process of developing iris recognition algorithms, so individual developers are required to write the necessary software almost every time. Finally, segmentation performance is currently only measurable using manual evaluation, which is time consuming and prone to human error. This thesis presents a completely novel generic platform for the purposes of developing, testing and evaluating iris recognition algorithms which is designed to simplify the process of developing and testing iris recognition algorithms. Existing open-source algorithms are integrated into the generic platform and are evaluated using the results it produces. Three iris recognition segmentation algorithms and one normalisation algorithm are proposed. Three of the algorithms increased true match recognition performance by between two and 45 percentage points when compared to the available open-source algorithms and methods found in the literature. A matching algorithm was developed that significantly speeds up the process of analysing the results of encoding. Lastly, this work also proposes a method of automatically evaluating the performance of segmentation algorithms, so minimising the need for manual evaluation.