基于类间散布矩阵的二维主分量分析

主分量分析是一种线性特征抽取方法，被广泛地应用在人脸等图像识别领域。但传统的PCA都以总体散布矩阵作为产生矩阵，并且要将作为图像的矩阵转换为列向量进行计算。该文给出了一种利用图像矩阵直接计算的二维PCA，以类间散布矩阵的本征向量作为投影方向，取得了比利用总体散布矩阵更好的识别效果，并且特征抽取速度更快。在ORL和NUSTFDBⅡ标准人脸库上的实验验证了该方法的有效性。

A Two-dimensional PCA Based on Between-class Scatter Matrix

Principal component analysis (PCA) is an important method widely used in images data compression and feature extraction. But conventional PCA usually uses total scatter matrix as a generation matrix, and two-dimension (2D) image matrices must be transformed into vectors. This paper gives a 2D-PCA, which uses original image matrices to compute between-class covariance matrix and its eigenvectors are derived for images feature extraction. The experiments on ORL and NUSTFDBⅡface-databases indicate that the recognition rates are higher than PCA and 2D-PCA using total scatter matrix, and the speed of feature extraction is faster.