Naslov
Estimating and Fusing Quality Factors for Iris Biometric Images

Autori
Kalka, Nathan D. ; Jinyu, Zuo ; Schmid, Natalia A. ; Čukić, Bojan

Izvornik
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans (1083-4427) 40 (2010), 3; 509-524

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Belief function; Dempster–Shafer (DS) theory; Iris image quality; Iris recognition; Quality metrics

Sažetak
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 poorquality 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, offangle, 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(n2 log n).

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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