Learning to predict match scores for iris image quality assessment

Abstract

Individual image quality metrics that focus on a particular form of image degradation have the virtue of being readily decipherable but also the drawback of not relating directly to the purpose for which the image is used. We describe here a method for learning the quality of iris images from the output of iris matching algorithms. We extract a large number of image quality features forming a high dimensional feature vector and label each training image with the match score for its corresponding genuine image in the enrolled database. We then train a Random Forest regressor to predict this match score, and in the course of training apply feature selection to dramatically reduce the feature vector dimensionality. A comparison of several alternative methods of feature selection is given. Our method demonstrates that the effects of image quality degradation on match scores can be predicted from image features. The predicted genuine match score serves as a quality metric, enabling filtering of poor quality images before enrollment or identification.

Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

“…In [23], the result of the iris segmentation module is used to form a quality score. Happold et al [24] proposed a method for predicting the iris matching scores of an iris image pair based on their quality features. They calculated these features for precisely segmented iris images.…”

“…In summary, some of the methods for iris quality assessment, such as [25,26], are proposed for NIR images, and only a few types of distortion are considered. Some other quality metrics, like those in [3,23,24], require a segmented iris image to calculate their quality features. They also take limited distortion types into account and are not expected to work well for quality assessment of authentic iris images taken in visible light in arbitrary environmental conditions.…”