Letters: Palmprint recognition with 2DPCA+PCA based on modular neural networks

Authors:
Zhong-Qiu Zhao;De-Shuang Huang;Wei Jia
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei Anhui 230031, China and Department of Automation, University of Science and Te ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei Anhui 230031, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei Anhui 230031, China and Department of Automation, University of Science and Te ...
Venue:
Neurocomputing
Year:
2007

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Abstract

In this letter, a novel modular neural network (MNN) classifier, which partitions a K-class problem into many much easier two-class problems in sub-subspaces, was proposed to perform palmprint recognition. Moreover, in order to make palmprint recognition more accurate, we introduced 2DPCA technique into the extraction of palmprint features, and removed the illumination information from the collected palm images using w/o3 technique. Our approach was compared with several existing methods, and obtained a satisfying classification performance on the Hong Kong Polytechnic University (PolyU) Palmprint Database.