Research of Palmprint Recognition Based on 2DPCA

Authors:
Haifeng Sang;Weiqi Yuan;Zhijia Zhang
Affiliations:
Shenyang University of Technology Computer Vision Group, Shenyang, China 110178;Shenyang University of Technology Computer Vision Group, Shenyang, China 110178;Shenyang University of Technology Computer Vision Group, Shenyang, China 110178
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

A feature extraction method of palmprint recognition based on Two-Dimensional Principal Component Analysis (2DPCA) is proposed in this work. A series of experiments were performed on the PolyU- Online- Palmprint ---Database with a nearest neighbor classifier and cosine distance. The recognition rate is 99.14%. The 2DPCA method has more recognition accuracy and more computationally efficient than PCA, especially in the small training samples. At the same time the selection of threshold has been researched in different application systems.