Statistical Non-Uniform Sampling of Gabor Wavelet Coefficients for Face Recongnition
Shan Du, R.K. Ward · 2006
A statistics based, non-uniform sampling of the Gabor wavelet decomposition coefficients for face recognition is presented in this paper. Gabor wavelets are popularly used to decompose face images into their spatial/frequency domains. The derived Gabor coefficients generate an augmented vector, e.g., 40 times larger than the original gray-scale vector. To reduce the dimensionality, uniform sampling of the Gabor coefficients is normally used. In this paper, we propose a non-uniform sampling method of the Gabor coefficients such that the coefficients corresponding to important face features are sampled much finer than those of the other parts of the image. The non-uniform sampling is based on the local statistics of the Gabor coefficients obtained from a set of training images. This adaptation is implemented in a hierarchical fashion; a coarse-to-fine strategy results in multi-level sampling rates. After the samples are obtained, the traditional principal component analysis (PCA) is used to code the samples for the final classification. The experimental results show that the proposed non-uniform sampling of Gabor coefficients outperforms the uniform one and the popular eigenfaces method.