Sampling Gabor features for face recognition
The Gabor feature is effective for facial image representation. However, the dimension of a Gabor feature vector is very high so that the computation and memory requirements are prohibitively large. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature. 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. 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.