Iris recognition using block local binary patterns and relational measures

Iris biometrics is widely used because of highly discriminative characteristics that are found in iris. But designing an iris recognition system which is invariant to extrinsic factors such as illumination, noise, camera-to-eye distance is fairly challenging. This paper proposes a novel iris recognition approach which takes into account the iris structure, illumination variation, occlusion, noise and rotational variance. A feature extraction technique exploiting the local iris features has been proposed that uses two features extracted from different blocks of an iris. The features are extracted from suitably modified pixel values which are enhanced to improve robustness of the technique. Relational measure is proposed that considers both radial and circumferential features which is combined with the block local binary pattern (BLBP). The BLBP is applied in an unconventional block-wise manner adaptive to the iris structure. Finally scores are fused at score level. Experimental results on two publicly available Casia Interval and Lamp databases as well as on our IITK iris database demonstrates the usefulness of the proposed system.