A new approach for iris segmentation

Iris segmentation is an important first step for high accuracy iris recognition. A robust iris segmentation procedure should be able to handle noise, occlusion and non-uniform lighting. It also impacts system accuracy — high FAR or FRR values may come directly from bad or wrong segmentations. In this paper a simple new approach for iris segmentation is proposed that tries to integrate quality evaluation ideas directly into the segmentation algorithm. By cutting out all the bad areas, the fraction of the iris that remains can be used as a comprehensive quality measure. This eliminates images with high occlusion (e.g. by the eyelids) as well as images with other quality problems (e.g. low contrast), all using the same mechanism. The proposed method has been tested on a medium-sized (450 image) public database (MMU1) and the score distribution investigated. We also show that, as expected, overall matching accuracy can be improved by rejecting images which have a low quality assessment, thus validating the utility of this measure.