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  • Research Article
  • Open Access

Information Theory for Gabor Feature Selection for Face Recognition

EURASIP Journal on Advances in Signal Processing20062006:030274

  • Received: 21 June 2005
  • Accepted: 26 September 2005
  • Published:


A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.


  • Feature Selection
  • Mutual Information
  • Face Recognition
  • Face Image
  • Kernel Method

Authors’ Affiliations

School of Computer Science and Information Technology, The University of Nottingham, Nottingham NG8 1BB, United Kingdom


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© Shen and Bai 2006