An Efficient Parallel Approach for Sclera Vein Recognition
Sclera vein recognition is 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. First, we designed a rotation- and scale-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. Second, we developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce GPU memory cost. Third, we designed a coarse-to-fine two-stage matching method. Finally, we developed a mapping scheme to map the subtasks to GPU processing units. The experimental results show that our proposed method can achieve dramatic processing speed improvement without compromising the recognition accuracy.