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.
© 2010 Chinese Optics LettersPDF Article