Limbus impact removal for off-angle iris recognition using eye models

The traditional iris recognition algorithms segment the iris image at the cornea-sclera border as the outer boundary because they consider the visible portion of iris as the entire iris texture. However, limbus, an additional semitransparent eye structure at junction of the cornea and sclera, occludes iris textures at the sides that cannot be seen at the off-angle iris images. In the biometrics community, limbus occlusion is unnoticed due to its limited effect at frontal iris images. However, to ignore the effect of the limbus occlusion in off-angle iris images causes significant performance degradation in iris biometrics. In this paper, we first investigate the limbus impact on off-angle iris recognition. Then, we propose a new approach to remove the effect of limbus occlusion. In our approach, we segmented iris image at its actual outer iris boundary instead of the visible outer iris boundary as in traditional methods and normalize them based on the actual outer iris boundary. The invisible iris region in unwrapped image that is occluded by limbus is eliminated by including it into the mask. Based on the relation between the segmentation parameters of actual and visible iris boundaries, we generate a transfer function and estimate the actual iris boundary from the segmented visible iris boundary depending on the known limbus height and gaze angle. Moreover, based on experiments with the synthetic iris dataset from the biometric eye model, we first show that not only the acquisition angle but also the limbus height negatively affects the performance of the off-angle iris recognition and then we eliminate this negative effect with applying our proposed method.