Sclera Segmentation and Recognition for Spectacle Dataset

Abstract

Sclera, a connective tissue enveloping the eye, emerges as a novel biometric recognition method for human identification. The composition of blood vessels in the sclera proves ideal for biometric use—visible with ease, stable over time, and unique to each individual. This paper proposes sclera segmentation and recognition techniques tailored for individuals wearing spectacles. The SSV (Spectacle Sclera Vision) Dataset was meticulously created to address challenges introduced by eyewear, including reflections, distortions, and illumination variations. The study explores the unique characteristics of the sclera region, presenting a comparative analysis of traditional and neural network-based segmentation and recognition methods on the SSV Dataset. Notably, Linear SVC outperforms CNN in recognition, and UNET demonstrates superior sclera segmentation compared to OTSU. The findings provide a foundation for potential advancements in developing robust multi-class classification models for sclera biometrics in real-world scenarios. Future work involves further analysis, scalability testing, and exploration of diverse applications in ocular health and security systems.