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 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.
Highlights
Daugman [1] presented evidence that visual neurons could optimize the general uncertainty relations for resolution in space, spatial frequency, and orientation
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
We proposed a procedure shown in Algorithm 1 to generate m extrapersonal samples using 40 (5 scales, 8 orientations) Gabor filters: instead of using only m pairs, our method randomly generates m samples from m × 40 extrapersonal image pairs
Summary
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 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. 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] saw the success of Gabor filters: both of the top two approaches used Gabor filters for feature extraction
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