Open Access

Information Theory for Gabor Feature Selection for Face Recognition

EURASIP Journal on Advances in Signal Processing20062006:030274

https://doi.org/10.1155/ASP/2006/30274

Received: 21 June 2005

Accepted: 26 September 2005

Published: 18 March 2006

Abstract

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.

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Authors’ Affiliations

(1)
School of Computer Science and Information Technology, The University of Nottingham

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Copyright

© Shen and Bai 2006