关键词: 小波变换, 梯度方向直方图, 线性判别分析

Abstract: Wavelet transforms-based Histogram of Oriented Gradient（HOG） with Linear Discriminant Analysis（LDA） for feature extraction is proposed since Eigenface and Fisherface do not work well to cope with small training sets of high dimension. In order to represent face features better and reduce dimension, wavelet transform could be used for extracting face features. The approximation coefficients are processed by HOG+LDA technique and mean square deviation is employed to handle horizontal, vertical and diagonal detail coefficients, respectively. The resulting features are discriminated by Euclidean distance. Simulation results conducted on the ORL and Yale databases show that the proposed method achieves excellent performance both in terms of classification accuracy and computational efficiency.

Key words: wavelet transforms, histograms of oriented gradient, linear discriminant analysis