Information Theory for Gabor Feature Selection for Face Recognition
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Information Theory for Gabor Feature Selection for Face Recognition Linlin Shen and Li Bai School of Computer Science and Information Technology, The University of Nottingham, Nottingham NG8 1BB, UK Received 21 June 2005; Revised 23 September 2005; Accepted 26 September 2005 Recommended for Publication by Mark Liao 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Daugman [1] presented evidence that visual neurons could optimize the general uncertainty relations for resolution in space, spatial frequency, and orientation. Gabor filters are believed to function similarly to the visual neurons of the human visual system. From an information-theoretic viewpoint, Okajima [2] derived Gabor functions as solutions for a certain mutual-information maximization problem. It shows that the Gabor receptive field can extract the maximum information from local image regions. Researchers have also shown that Gabor features, when appropriately designed, are invariant against translation, rotation, and scale [3]. Successful applications of Gabor filters in face recognition date back to the FERET evaluation competition [4], when the elastic bunch graph matching method [5] appeared as the winner. The more recent face verification competition [6] also saw the success of Gabor filters: both of the top two approaches used Gabor filters for feature extraction. For face recognition applications, the number of Gabor filters used to convolve face images varies with applications, but usually 40 filters (5 scales and 8 orientations) are used [5, 7–9]. However, due to the large number of convolution operations of Gabor filters with the image (convolution at each position of the image), the computation cost is pro-
hibitive. Even if a parallel system was used, it took about 7 seconds to convolve a 128 × 128 image with 40 Gabor filters [7]. For global methods (convolut
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