In recent years, computing power and biometric sensors have not only become more powerful, but also more affordable to the general public. In turn, there has been great interest in developing and deploying biometric personal ID systems. Unlike the conventional security systems that often require people to provide artificial identification for verification, i.e. password or algorithmic generated keys, biometric security systems use an individual's biometric measurements, including fingerprint, face, hand geometry, and iris. It is believed that these measurements are unique to the individual, making them much more reliable and less likely to be stolen, lost, forgotten, or forged.
Among these biometric measurements, the iris is regarded as one of the most reliable and accurate security approaches because it is an internal organ protected by the body's own biological mechanisms. It is easy to access, and almost impossible to modify without the risk of damaging the iris.
Although there have been significant advancements in developing iris-based identification processes during recent years, there remains significant room for improvement. This dissertation presents a novel approach to the iris localization and code matching. It uses a fixed diameter method and a parabolic curve fitting approach for locating the iris and eyelids as well as a k-d tree for iris matching. The iris recognition rate is improved by accurately locating the eyelids and eliminating the signal noise in an eye image. Furthermore, the overall system performance is increased significantly by using a partial iris image and taking the advantage of the k-d binary tree.
We present the research results of four processing stages of iris recognition: localization, normalization, feature extraction, and code matching. The localization process is based on histogram analysis, morphological process, Canny edge detection, and parabolic curve fitting. The normalization process adopts Daugman's rubber-sheet approach and converts the iris image from Cartesian coordinators to polar coordinates. In the feature extraction process, the feature vectors are created and quantized using 1-D Log-Gabor wavelet. Finally, the iris code matching process is conducted using a k-dimensional binary tree and Hamming distance.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1345 |
| Date | 01 January 2009 |
| Creators | Zhou, Steven |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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