An automated method for predicting iris segmentation failures

Abstract

Arguably the most important task in iris recognition systems involves localization of the iris region of interest, a process known as iris segmentation. Research has found that segmentation results are a dominant factor that drives iris recognition matching performance. This work proposes techniques based on probabilistic intensity features and geometric features to arrive at scores indicating the success of both pupil and iris segmentation. The technique is fully automated and therefore requires no human supervision or manual evaluation. This work also presents a machine learning approach which utilizes the pupil and iris scores to arrive at an overall iris segmentation result prediction. We test the techniques using two iris segmentation algorithms of varying performance on two publicly available iris datasets. Our analysis shows that the approach is capable of arriving at segmentation scores suitable for predicting both the success and failure of pupil or iris segmentation. The proposed machine learning approach achieves an average classification accuracy of 98.45% across the four combinations of algorithms and datasets tested when predicting overall segmentation results. Finally, we present one potential application of the technique specific to iris match score performance and outline many other potential uses for the algorithm.