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

  • Authors:

  • Yimo Guo;Jie Chen;Guoying Zhao;Matti Pietikäinen;Zhengguang Xu

  • Affiliations:

  • Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland FIN-90014 and School of Information Engineering, University of Science and Technology Beijin ...;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland FIN-90014;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland FIN-90014;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland FIN-90014;School of Information Engineering, University of Science and Technology Beijing, Beijing, China 100083

  • Venue:
  • SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
  • Year:
  • 2009

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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.