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Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains ix, 109 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 106-109).

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains xiii, 58 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 51-58).

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Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 92 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 89-92).

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M.Ing. (Electrical And Electronic Engineering) / Iris recognition systems have attracted much attention for their uniqueness, stability and reliability. This recognition system is composed of four main modules, namely, iris acquisition, iris segmentation, feature extraction and encoding and - nally iris matching. However, performance of this system is a ected by poor image quality. In this research, a novel iris image quality assessment method based on character component is presented. This method is composed of two steps, individual assessment of character quality parameters and fusion of estimated quality parameters using Principal Component Analysis (PCA). The de ned quality parameters considered in this research are entropy, sharpness, occlusion, dilation, area ratio, contrast and blur. The designed technique was tested on three databases: Chinese Academy of Science Institute of Automation (CASIA), University of Beira Interior (UBIRIS) and Internal Collection (IC). Individual assessment of quality parameters has shown that dilation, sharpness and blur have more in uence on the quality score than the other parameters. The images were classi ed into two categories (good and bad) by human visual inspection. The e ect of the individual parameters on each database is illustrated, with CASIA exhibiting higher quality scores than the UBIRIS and IC databases. Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) were used to evaluate the performance of the proposed quality assessment algorithm. A k-fold cross validation technique was employed to the classi ers to obtain unbiased results. Two performance measures were used to rate the proposed algorithm, namely, Correct Rate (CR) and Area Under the Curve (AUC). Both performance measures showed that SVM classi er outperforms LDA in correctly classifying the quality of the images in all three databases. The experimental results demonstrated that the proposed algorithm is capable of detecting poor quality images as it yields an e ciency of over 84% and 90% in CR and AUC respectively. The use of character component to assess quality has been found to be su cient, though there is a need to develop a better technique for standardization of quality. The results found using a SVM classi er a rms the proposed algorithm is well-suited for quality assessment.