Feature information based quality measure for iris recognition

In this paper, feature information based quality measure is proposed to measure the quality of an iris image. The proposed method automatically selects regions of the iris with most changing patterns to assess the quality based on the feature information. The Log-Gabor filter is used as a bandpass filter to extract feature information of the selected iris regions. A blurry image will be more homogenous than a clear image. In other words, the features in a blurry image will be closer to uniform distribution. Therefore, information distance between the selected feature distribution and uniform distribution can be used to generate the feature information score. This score is then fused with occlusion and pupil dilation measures to obtain a quality score. We analyzed the CASIA database ver. 2.0 images using this proposed method. The quality scores are consistent with our observations. Moreover, we evaluated the relationship between the quality score and the recognition accuracy. The experimental results show that the proposed quality score is highly correlated with the recognition accuracy. In addition, we compared our method with Daugman's quality measure; the experimental results show that this proposed quality measure can effectively measure a variety of iris images including images with a small region of changing patterns, and images heavily occluded and/or blurred. The experimental results also show that this proposed method is insensitive to residual eyelids and eyelashes from segmentation errors.