A Neural Approach for Sclera Vein Recognition

Abstract: This research involves the study of Sclera vein recognition, its shown to be a promising method for human identification. However, its matching speed is slow, which could impact its application for real-time applications. To improve the matching efficiency, we proposed a new parallel sclera vein recognition method using a two-stage parallel approach for registration and matching. We designed a rotation- and scale-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. We developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce memory cost. We designed a coarse-to-fine two-stage matching method. The experimental results show that our proposed method can achieve dramatic processing speed improvement without compromising the recognition accuracy.

Keywords: Line Descriptor, Morphological, Gabor Filter, Vein Pattern Enhancement, Feature Extraction, Template Matching.