Estimating and Fusing Quality Factors for Iris Biometric Images

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris qu...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 40; no. 3; pp. 509 - 524
Main Authors Kalka, Nathan D, Jinyu Zuo, Schmid, Natalia A, Cukic, Bojan
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Belief function
Biometrics
Dempster-Shafer (DS) theory
Illumination
Image quality
iris image quality
Iris recognition
Lighting
Motion estimation
Occlusion
Optical reflection
Pattern recognition
Performance analysis
Pixel
Q factor
Quality assessment
Quality factor
quality metrics
Recognition
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Summary:Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris quality assessment research by analyzing the effect of various quality factors such as defocus blur, off-angle, occlusion/specular reflection, lighting, and iris resolution on the performance of a traditional iris recognition system. We further design a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts. First, each factor is estimated individually, and then, the second step fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning. The designed block is evaluated on three data sets: Institute of Automation, Chinese Academy of Sciences (CASIA) 3.0 interval subset, West Virginia University (WVU) non-ideal iris, and Iris Challenge Evaluation (ICE) 1.0 dataset made available by National Institute for Standards and Technology (NIST). Considerable improvement in recognition performance is demonstrated when removing poor-quality images selected by our quality metric. The upper bound on computational complexity required to evaluate the quality of a single image is O(n 2 log n).
Bibliography:ObjectType-Article-2
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2010.2041658