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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.
Abstract-The problem of face verification across the short wave infrared spectrum (SWIR) is studied in order to illustrate the advantages and limitations of SWIR face verification. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of SWIR cross-spectral matching. Experiments also show that images captured under different SWIR wavelengths can be matched to visible images with promising results. The role of multispectral fusion in improving recognition performance in SWIR images is finally illustrated. To the best of our knowledge, this is the first time cross-spectral SWIR face recognition is being investigated in the open literature.
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.
In this paper we study the problem of cross spectral face recognition in heterogeneous environments. Specifically we investigate the advantages and limitations of matching short wave infrared (SWIR) face images to visible images under controlled or uncontrolled conditions. The contributions of this work are three-fold. First, three dif f erent databases are considered, which represent three dif f erent data col lection conditions, i.e., images acquired in fully controlled (indoors), semi-controlled (indoors at standoff distances ?: 50m), and uncontrolled (outdoor operational conditions) environments. Second, we demonstrate the possibility of SWIR cross-spectral matching under controlled and chal lenging scenarios. Third, we illustrate how photometric normalization and our proposed cross-photometric score level fusion rule can be utilized to improve cross-spectral matching peiformance across all scenarios. We utilized both commercial and academic (texture-based) face match ers and performed a set of experiments indicating that SWIR images can be matched to visible images with encouraging results. Our experiments also indicate that the level of im provement in recognition performance is scenario depen dent.
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