A comprehensive sciera image quality measure
Sciera recognition can be used for human identification. However, if the sciera images cannot be properly segmented by the system or the images of sciera patterns are defocused and/or saturated, it can significantly affect the accuracy of sciera recognition. In this paper, we propose a comprehensive sciera image quality measure which can quickly detect if the image has a valid eye1, assess the image quality, evaluate the segmentation accuracy, and measure if the image has sufficient feature information for recognition. In addition, it used Dempster Shafer Theory to fuse the quality score, segmentation score, and feature score together to generate the overall combination score. It is empirically verified using the UBIRIS database that the proposed quality measure is highly correlated with the performance of sciera recognition.