Abstract
Iris image quality critically determines iris recognition performance and the quality metrics of iris images are also useful prior information for adaptive selection of optimal recognition strategy. Iris image quality is jointly determined by multiple factors such as focus, occlusion, off-angle, deformation, etc. So it is a complex problem to assess the overall quality score of an iris image. This paper proposes a novel framework for comprehensive assessment of iris image quality. The contributions of the paper include three aspects: (i) Three novel approaches are proposed to estimate the quality metrics (QM) of defocus, motion blur and off-angle in an iris image respectively, (ii) A fusion method based on likelihood ratio is proposed to combine six quality factors of an iris image into an unified quality score. (iii) A statistical quantization method based on t-test is proposed to adaptively classify the iris images in a database into a number of quality levels. Extensive experiments demonstrate the proposed framework can effectively assess the overall quality of iris images. And the relationship between iris recognition results and the quality level of iris images can be explicitly formulated.
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