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A Novel Feature Extraction Approach to Face Recognition Based on Partial Least Squares Regression

  • Yuan-Yuan Wan
  • Ji-Xiang Du
  • Kang Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In this paper, partial least square (PLS) regression is firstly employed in image processing. And a new technique coined partial least squares (PLS) regression, line-based PLS, is proposed for feature extraction of the images. To test this new approach, a series of experiments were performed on the famous face image database: ORL face database. Compared with newly proposed two dimensional principal component analysis (2DPCA), it can be found that the dimension of the feature vectors of the line-based PLS is no more than half of the 2DPCA’s while the recognition rate can retain at the same high level. Thus, the feature extraction based on line-based PLS regression is a feasible and effective method.

Keywords

Feature Vector Partial Little Square Face Recognition Recognition Accuracy Latent Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuan-Yuan Wan
    • 1
    • 2
  • Ji-Xiang Du
    • 1
    • 2
  • Kang Li
    • 3
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHeFeiChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  3. 3.School of Electrical & Electronic EngineeringQueen’s UniversityBelfastUK

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