Segmentation and Recognition of Sclera trait using Adaptive Gabor Filter

Abstract

The rise in digital transition of connecting world via various online platforms has brought us to think on how to protect what’s most important in this digital era. Among various security devices and applications available, biometrics is getting its attention on the smallest of devices possible to enhance the protection. Among various biometric technology, sclera is considered to be the newest trait researched today. In this paper, we propose an efficient sclera segmentation and recognition technique. We explored the use of adaptive Gabor filters to extract the texture features from sclera trait. The extracted texture features are represented in the form of bit strings. The similarity between the two bit strings are measured using normalized Hamming distance. The evaluation of the proposed method to check its efficiency is done using SSRBC 2016 dataset. The result of the proposed method is effective and feasible in sclera segmentation and recognition.