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Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. Considerable improvement in recognition performance is demonstrated when removing poor quality images evaluated by our quality metric. The upper bound on processing complexity required to evaluate quality of a single image is O(n 2 log n), that of a 2D-Fast Fourier Transform. This work wouldn't have been possible without the help, encouragement, tutelage, and guidance bestowed upon me over the past year of my Master's program. I would like to take this opportunity to thank those individuals for their time and patience. Firstly, I would like to express a deep heartfelt thanks to my advisor and committee chair, Dr. Natalia Schmid. She has been very patient with me and has provided me with invaluable guidance and encouragement, not only towards the completion of this work, but as a researcher. My graduate Committee members, Dr. Bojan Cukic and Dr. Larry Hornak deserve a heartfelt thanks as well, for their constructive criticisms and invaluable feedback.