Rotation-Independent IRIS Matching by Motion Estimation

In this paper we propose a novel method of applying motion estimation techniques to human authentication by iris matching. By exploiting the inherent differences in vector fields generated by comparing same-class and different-class irises, good matching performance was obtained. The method was applied to 600 images of 150 eyes from the Bath database. The best settings of several parameters were determined through experimental minimization of equal error rate (EER), which was estimated from the matching and nearest non matching distributions. The effect of iris rotation was studied through circular shifts and seen to have minimal effects on match/non match scores. The standard deviation of the X-vector data was found to give best performance with 100% correct recognition rate (CRR) and a flat receiver operating characteristics (ROC) indicating no false accepts or rejects within the data with an estimated EER of 0.007. Images compressed with JPEG2000 at 0.5 bpp were similarly processed resulting in an EER of 0.014 at a normalized image size of 1536 bytes.