Experimental analysis regarding the influence of iris segmentation on the recognition rate
This study addresses the practical problem of optimizing iris recognition systems for biometric security applications, but it is incremental as it focuses on analyzing existing methods rather than introducing new ones.
The authors investigated whether improving iris segmentation accuracy consistently enhances overall iris recognition performance, and systematically evaluated the impact of specific segmentation parameters on the biometric tool chain.
In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.