Computer Vision and Image Understanding
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Data quality assessment is a key issue, in order to broaden the applicability of iris biometrics to unconstrained imaging conditions. Previous research efforts sought to use visible wavelength (VW) light imagery to acquire data at significantly larger distances than usual and on moving subjects, which makes this real-world data notoriously different from the acquired in the near-infrared setup. Having empirically observed that published strategies to assess iris image quality do not handle the specificity of such data, this paper proposes a method to assess the quality of VW iris samples captured in unconstrained conditions, according to the factors that are known to determine the quality of iris biometric data: focus, motion, angle, occlusions, area, pupillary dilation, and levels of iris pigmentation. The key insight is to use the output of the segmentation phase in each assessment, which permits us to handle severely degraded samples that are likely to result of such imaging setup. Also, our experiments point that the given method improves the effectiveness of VW iris recognition, by avoiding that poor quality samples are considered in the recognition process.