Multi-band Gradient Component Pattern (MGCP): A New Statistical Feature for Face Recognition

  • Yimo Guo
  • Jie Chen
  • Guoying Zhao
  • Matti Pietikäinen
  • Zhengguang Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

A feature extraction method using multi-frequency bands is proposed for face recognition, named as the Multi-band Gradient Component Pattern (MGCP). The MGCP captures discriminative information from Gabor filter responses in virtue of an orthogonal gradient component analysis method, which is especially designed to encode energy variations of Gabor magnitude. Different from some well-known Gabor-based feature extraction methods, MGCP extracts geometry features from Gabor magnitudes in the orthogonal gradient space in a novel way. It is shown that such features encapsulate more discriminative information. The proposed method is evaluated by performing face recognition experiments on the FERET and FRGC ver 2.0 databases and compared with several state-of-the-art approaches. Experimental results demonstrate that MGCP achieves the highest recognition rate among all the compared methods, including some well-known Gabor-based methods.

Keywords

Face Recognition Receive Operator Characteristic Local Binary Pattern Gabor Wavelet Discriminative Information 
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 2009

Authors and Affiliations

  • Yimo Guo
    • 1
    • 2
  • Jie Chen
    • 1
  • Guoying Zhao
    • 1
  • Matti Pietikäinen
    • 1
  • Zhengguang Xu
    • 2
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluFinland
  2. 2.School of Information EngineeringUniversity of Science and Technology BeijingBeijingChina

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