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International Symposium on Neural Networks

ISNN 2009: Advances in Neural Networks – ISNN 2009 pp 407–415Cite as

Face Image Recognition Combining Holistic and Local Features

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

Abstract

This paper introduces a method using the holistic and the local features for face image recognition. The holistic feature is extracted from spatial domain by 2DPCA and the local feature is taken from 2D-DCT-frequency domain by 2DNMF, respectively. 2D-DCT coefficients form the different frequency components and get energy concentrate at the same time, which may be suitable to preserve some useful puny features often ignored in global method. And it may avoid the correlation between global and local features and offer complementary frequency information to spatial one. Finally, LSSVM regression is used to weight the mixed feature vectors and classify images. Experimental results have demonstrated the validity of the new method, which outperforms the conventional 2D-based PCA and NMF methods on ORL and JAFFE face databases.

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Author information

Authors and Affiliations

  1. College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Chen Pan

  2. College of Science, China Jiliang University, Hangzhou, 310018, China

    Feilong Cao

Authors
  1. Chen Pan
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  2. Feilong Cao
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Pan, C., Cao, F. (2009). Face Image Recognition Combining Holistic and Local Features. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_44

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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