The accuracy of a biometrics system increases with the quality of the sample used for identification. All current iris biometrics systems capture multiple samples that must be processed to identify a single, ideal image to be used for identification. Many metrics exist to evaluate the quality of an iris image. This thesis evaluates current metrics and introduces a method for determining the ideal iris image from a set of iris images by using the Mean Self-Match algorithm to examine the set of true matches. This proposed method is shown to outperform other methods currently used for selecting an ideal image from a set of iris images. The application of this method to face biometrics is also examined.
Quality Metrics for Biometrics
Master's Thesis
Abstract
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Author | James S Doyle |
Advisor | Dr. Patrick Flynn |
Contributor | Dr. Guarav Aggarwal, Committee Member |
Contributor | Dr. Patrick Flynn, Committee Chair |
Contributor | Dr. Kevin Bowyer, Committee Member |
Degree Level | Master's Thesis |
Degree Discipline | Computer Science and Engineering |
Degree Name | MSCSE |
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Submission Date | 2011-04-12 |
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Record Visibility | Public |
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Files
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DoyleJS092011T.pdf | 23.9 MB | application/pdf | Private |
At the request of the author, this Master's Thesis is not available to the public. You may request permission to view this file from the Publications Manager of the Graduate School. |
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