吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 828-833.doi: 10.13229/j.cnki.jdxbgxb201403040

• Orignal Article • Previous Articles     Next Articles

Face recognition based on eigen weighted modular two-directional two-dimensional PCA

GU Bo-yu,SUN Jun-xi,LI Hong-zuo,LIU Hong-xi,LIU Guang-wen   

  1. School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2013-06-18 Online:2014-03-01 Published:2014-03-01

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Abstract: An improved modular Two-directional Two-dimensional Principle Component Analysis (2D(PCA)) with eigen weight for face recognition is proposed. First, the image is divided into sub-blocks and the features are extracted by modular 2D(PCA). Then, the feature weight is assigned to each sub-block of every image according to the contribution for recognition of the sub-block. Finally, the testing samples are classified by nearest neighborhood classification of weighted distance. The contribution of each sub-block is determined self-adaptively according to the proportion of local feature information in eigen space. This algorithm does not need any prior knowledge. Experimental results show that the recognition rate of the proposed algorithm is effectively improved.

Key words: information processing, pattern recognition, face recognition, principal component analysis(PCA), eigen weighted, feature extraction

CLC Number: 

  • TN911.73
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