基于曲波变换算法的手掌静脉特征识别系统研究
摘要
A novel personal recognition system utilizing palm vein patterns and a novel technique to analyze these vein patterns is presented. The technique utilizes the curvelet transform to extract features from vein patterns to facilitate recognition. This technique provides optimally sparse representations of objects along the edges. Principal component analysis (PCA) is applied on curvelet-decomposed images for dimensionality reduction. A simple distance-based classifier, such as the nearest-neighbor (NN) classifier, is employed. The experiments are performed using our palm vein database. Experimental results show that the algorithm reaches a recognition accuracy of 99.6% on the database of 500 distinct subjects.
引用本文（GB/T 7714）
Qiang Li, Yanan Zeng, Xiaojun Peng, 等. 基于曲波变换算法的手掌静脉特征识别系统研究[J]. Chinese Optics Letters, 2010.
引文网络
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