Optimal sampling of Gabor features for face recognition

The Gabor feature is effective for facial image representation, while linear discriminant analysis (LDA) can extract the most discriminant information from the Gabor feature for face recognition. In practice, the dimension of a Gabor feature vector is so high that the computation and memory requirements are prohibitively large. To reduce the dimension, one simple scheme is to extract the Gabor feature at sub-sampled positions, usually in a regular grid, in a face region. However, this scheme is not effective enough and degrades the recognition performance. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature such that the number of feature points is as small as possible while the representation capability of the points is as high as possible. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of principal component analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition, i.e., PCA is first used to reduce the dimension of the Gabor feature vectors generated from the sub-sampled positions, and then a common LDA is applied. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.