ROBUST SCLERA AND IRIS RECOGNITION TECHNIQUE FOR SECURITY SYSTEMS

Abstract

The vein structure in sclera, a white and opaque outer protective covering of eye, is anecdotally stable over
time and unique to each person. As a result, it is well be suited for use as a biometric for human identification. The few
researchers have performed sclera vein pattern recognition and have reported promising, but the low accuracy, and the
initial results. Sclera recognition poses several challenges: the vein structure moves and then deforms with the movement
of the eye and its surrounding tissues; images of sclera patterns are often defocused and/or saturated; and, most
importantly, a vein structure in the sclera is multi-layered and has complex non-linear deformation. The previous
approaches in the sclera recognition have treated the sclera patterns as a one-layered vein structure, and, as a result,
their sclera recognition accuracy is not high. In this, we propose a new method for sclera recognition with the following
contributions: First, we developed a color-based sclera region estimation scheme for the sclera segmentation. Second,
we designed a Gabor wavelet based sclera pattern enhancement method, and an adaptive thresholding method to
emphasize and binarize a sclera vein patterns. Third, we proposed a line descriptor based feature extraction,
registration, and matching method that is scale-, orientation-, and deformation-invariant, and can mitigate the multilayered deformation effects and tolerate the segmentation error. It is empirically verified using UBIRIS and IUPUI multiwavelength databases that the proposed method can perform accurate sclera recognition. In addition, the recognition
results are compared to the iris recognition algorithms, with the very comparable results.