Multispectral scleral patterns for ocular biometric recognition

Biometrics is the science of recognizing people based on their physical or behavioral traits such as face, fingerprints, iris, and voice. Among these characteristics, ocular biometrics has gained popularity due to the significant progress made in iris recognition. However, iris recognition is unfavorably influenced by the non-frontal gaze direction of the eye with respect to the acquisition device. In such scenarios, additional parts of the eye, such as the sclera (the white of the eye) may be of significance. In this article, we investigate the use of the sclera texture and vasculature patterns evident in the sclera as a potential biometric. Iris patterns are better discerned in the near infrared spectrum (NIR) while vasculature patterns are better discerned in the visible spectrum (RGB). Therefore, multispectral images of the eye, consisting of both NIR and RGB channels, are used in this work in order to ensure that both the iris and the vasculature patterns are imaged. The contributions of this work include: (a) the assembling of a multispectral eye database to initiate research on this topic; (b) the design of a novel algorithm for sclera segmentation based on a normalized sclera index measure; and (c) the evaluation of three different feature extraction and matching schemes on the assembled database in order to examine the potential of utilizing the sclera and the accompanying vasculature pattern as biometric cues. Experimental results convey the potential of this biometric in an ocular-based recognition system.

► Sclera texture can add complementary information to non-frontal iris for biometric recognition.
► Multispectral image of the eye is used for designing a sclera biometric system.
► A new algorithm is designed for sclera segmentation based on a normalized sclera index.
► We study three different feature extraction and matching schemes for sclera texture.
► The results convey the potential of this biometric for ocular recognition.