A comprehensive sciera image quality measure

Sclera recognition can be used for human identification. However, if the sclera images cannot be properly segmented by the system or the images of sclera patterns are defocused and/or saturated, it can significantly affect the accuracy of sclera recognition. In this paper, we propose a comprehensive sclera 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 sclera recognition.