Effect of segmentation errors on off-angle iris recognition

Abstract

This paper investigates the effect of segmentation errors on off-angle iris recognition. We first segment the inner and outer boundaries of off-angle iris images by fitting the best ellipses to boundaries. Then, we validate the segmentation results using Ground Truth Tool and fix the possible errors. Second, we add random errors on iris segmentation parameters to examine how Hamming distance distribution changes for different amount of segmentation error. In order to add errors, we first group the parameters into three sets as ellipse center (i.e., x, y), minor and major axis (i.e., r 1 , r 2 ) and orientation (θ) and changed the parameters by adding positive and negative random error as a noise to the ground truth segmentation parameters. Our purpose is to show how performance of iris recognition is affected if there is an error in segmentation and how important is to have a very robust segmentation algorithm for off-angle iris recognition. We test the effect of segmentation errors using our off-angle iris dataset that contains images from -50° to +50° in gaze angles. Based on our experimental results, we observed that error in ellipse center decrease the recognition performance worse than minor and major axis and orientation. The main reason is the normalization process tolerates the small amount of error in minor and major axis and bit shift in matching handles the small variations in the orientation errors. However, if the error in the ellipse orientation exceeds the limit of the bit shift method tolerance, the performance of the off-angle iris recognition dramatically drops.