Annular iris recognition using SURF

Abstract

This paper proposes an iris recognition system which can handle efficiently the problem of rotation, scaling, change in gaze of individual and partial occlusions that are inherent to non-restrictive iris imaging system. In addition to this, traditional iris normalisation approach deforms texture features linearly due to change in camera to eye distance or non-uniform illumination. To overcome the effect of aliasing features are extracted directly from annular region of iris using Speeded Up Robust Features (SURF). These features are invariant to transformations and occlusion. The system is tested on BATH, CASIA and IITK databases and is showing an accuracy of more than 97%. From the results it is inferred that local features from annular iris gives much better accuracy for poor quality images in comparison to normalised iris. © 2009 Springer-Verlag Berlin Heidelberg.