Iris recognition in less constrained environments: a video-based approach
thesisposted on 02.03.2017 by Mahadeo, Nitin Kumar
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
This dissertation focuses on iris biometrics. Although the iris is the most accurate biometric, its adoption has been relatively slow. Conventional iris recognition systems utilize still eye images captured in ideal environments and require highly constrained subject presentation. A drop in recognition performance is observed when these constraints are removed as the quality of the data acquired is affected by heterogeneous factors. For iris recognition to be widely adopted, it can therefore be argued that the image capture must be facilitated and better performance should be achieved in less constrained imaging conditions. The research work presented in this dissertation demonstrates how performance in iris recognition systems is improved by adopting a video-based approach. The following components have been investigated in this study and presented in relevant publications: (1) Robust eye extraction method of eye images in face videos captured at a distance and on the move (2) Selection of optimal frames in iris videos (3) Iris segmentation in less constrained environments (4) An automated method for predicting inaccurate iris segmentation (5) Optimization of iris codes for improved recognition. The main results and novelties of this work include: Firstly, the development of a fast and accurate method for detecting eye images in face videos. Secondly, this work demonstrates that selection of optimal frames in NIR iris videos lead to better recognition performance. Thirdly, an accurate and robust iris segmentation model for eye images captured in uncontrolled conditions is proposed. Fourthly, this research presents a fully automated segmentation evaluation model for detection of in- correctly segmented iris images. Finally, a new method for optimization of several iris codes into a single highly optimized iris code is introduced. Our results and experiments suggest that incorporation of the above methods in traditional iris recognition systems will be useful for the adoption of this technology by a larger community.