Author Affiliations
Abstract
College of Optoelectronic Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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.
数字曲波变换 PCA 静脉特征 100.5010 Pattern recognition 100.3005 Image recognition devices 100.7410 Wavelets Chinese Optics Letters
2010, 8(6): 577