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 Third, we designed a coarse-to-fine two-stage matching method. Finally, we developed an efficient approach for sclera vein recognition with high accuracy. The experimental results show that our proposed method can achieve dramatic processing speed improvement without compromising the recognition accuracy.

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