A new approach for sclera vein recognition

The vein structure in the sclera is stable over time, unique to each person, and well suited for human identification. A few researchers have performed sclera vein pattern recognition and reported promising initial results. Sclera recognition poses several challenges: the vein structure moves and deforms with the movement of the eye; images of sclera patterns are often defocused and/or saturated; and, most importantly, the vein structure in the sclera is multi-layered and has complex non-linear deformation. In this paper, we proposed a new method for sclera recognition: First, we developed a color-based sclera region estimation scheme for sclera segmentation. Second, we designed a Gabor wavelet-based sclera pattern enhancement method, and an adaptive thresholding method to emphasize and binarize the sclera vein patterns. Third, we proposed a line descriptor-based feature extraction, registration, and matching method that is illumination-, scale-, orientation-, and deformation-invariant, and can mitigate the multi-layered deformation effects exhibited in the sclera and tolerate segmentation error. It is empirically verified using the UBIRIS database that the proposed method can perform accurate sclera recognition.