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Face Recognition by Using Overlapping Block Discriminative Common Vectors

  • Conference paper
Intelligent Science and Intelligent Data Engineering (IScIDE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7751))

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Abstract

Discriminative Common Vectors (DCV) has been widely used in face recognition. Previous literatures show that DCV can outperform PCA or LDA in classification accuracy of face images. In this paper, the author proposes a novel block DCV method, i.e. overlapping block DCV. This method first partitions every image into a number of blocks and views each block as a sample. Calculating the covariance matrix and solving its eigen values and eigenvectors are similar to PCA. Then the method chooses any sample from each class and projects it onto the null space to obtain the Common Vectors. DCV takes the Common Vectors as transform axes and exploits the transform axes to perform feature extraction. Compared with conventional DCV, overlapping block DCV seems to be more robust to the variation of facial details such as facial expression and can obtain a higher classification accuracy for face recognition.

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Author information

Authors and Affiliations

  1. Department of Computer Application and Technology, HanShan Normal University, Chaozhou, Guangdong Province, China

    Xiaohui Wang

Authors
  1. Xiaohui Wang
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Editor information

Editors and Affiliations

  1. Nanjing University of Science & Technology, 210094, Nanjing, China

    Jian Yang

  2. Department of Psychology, Peking University, 100871, Beijing, China

    Fang Fang

  3. School of Automation, Southeast University, 210096, Nanjing, P.R. China

    Changyin Sun

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Wang, X. (2013). Face Recognition by Using Overlapping Block Discriminative Common Vectors. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_30

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  • DOI: https://doi.org/10.1007/978-3-642-36669-7_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36668-0

  • Online ISBN: 978-3-642-36669-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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