Multispectral Ocular Biometrics

Abstract

This chapter discusses the use of multispectral imaging to perform bimodal ocular recognition where the eye region of the face is used for recognizing individuals. In particular, it explores the possibility of utilizing the patterns evident in the sclera, along with the iris, in order to improve the robustness of iris recognition systems . Commercial iris recognition systems typically capture frontal images of the eye in the near-infrared spectrum . However, in non-frontal images of the eye, iris recognition performance degrades considerably. As the eyeball deviates away from the camera, the iris information in the image decreases, while the scleral information increases. In this work, we demonstrate that by utilizing the texture of the sclera along with the vascular patterns evident on it, the performance of an iris recognition system can potentially be improved. The iris patterns are better observed in near-infrared spectrum, while conjunctival vasculature patterns are better discerned in the visible spectrum. Therefore, multispectral images of the eye are used to capture the details of both the iris and the sclera. The contributions of this paper include (a) the assembly of a multispectral eye image collection to study the impact of intra-class variation on sclera recognition performance , (b) the design and development of an automatic sclera, iris, and pupil segmentation algorithm, and (c) the improvement of iris recognition performance by fusing the iris and scleral patterns in non-frontal images of the eye.