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Title: Personal identification based on live iris image analysis
Author: Zhang, Dexin
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2006
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Live iris image analysis based personal identification has received more and more attention in today's highly mobile and inter-connected society, with deteriorating situation in public security. Huge amount of work has been done and great progress achieved in this area. However, some critical problems still persist and significant work needs to be done before mass-scale deployment on national and international levels can be achieved. Recognition performance and the system speed are the two hot topics in iris recognition research. The research aim in this thesis is trying to answer the following two questions: (1) what kind of information within the iris textures could be utilized for authentication? (2) how the algorithms could be speeded up for real time system requirement? The state of the art in iris recognition technologies is reviewed at first and the research difficulties are pointed out. In order to carry out the research in Bath, the Bath Iris Recognition Research Environment is built based on extensive literature review and comparison. Then four algorithms are proposed with the aim of answering the two questions above. A fast and robust iris localization algorithm based on Random Sample Consensus (RANSAC) is proposed in Chapter four, which could not only maintain a high localization rate, but also perform the procedure much faster than the leading algorithms proposed by Daugman or Wildes. This work contributes to question 2. Chapter four also reported a fast and robust eyelid removal algorithm, which could be carried out at a very high speed while still keeping a satisfying correct removal rate. This work also contributes to question 2. Chapter five proposed a local frequency amplitude variation based iris coding algorithm, which could greatly reduce the processing time while still maintain a very high distinguishing capability. This work contributes to both question 1 and 2. An effective eyelash removal algorithm based on local area analysis has been proposed in chapter six. Unlike other previous eyelash removal methods, which generally tried to detect and mask the eyelashes or the eyelash areas, the proposed method recreates iris pixels occluded by eyelashes using information from their non-occluded neighbors. Extensive experiments and comparisons have been done to prove the effectiveness and low computation complexity of these four proposed algorithms. Through the research in this thesis, two possible answers to the questions proposed at the beginning of this thesis could be given: (1) Local image variation is the essential unique information for one iris class to be differentiated from another. Apart from utilizing the local phase or intensity variations, this thesis used the local frequency variation, which is also proved to be very effective. Statistically, the local iris image pixels are related to each other, which could be used to reconstruct occluded iris pixels. (2) The adoption of Fast Fourier Transform (FFT) makes the feature extraction procedure very time efficient. Thus the fast representation of local feature information is a very effective way to speed up the coding process. Also the adoption of fast iris image preprocessing techniques would contribute to the system speed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available