为了对图像进行最优压缩，提出了两步2维主成分分析方法进行特征提取，称为增强的2维主成分分析。增强的2维主成分分析首先对图像进行行方向的2维主成分分析，再进行列方向的2维主成分分析。增强的2维主成分分析对图像进行了行方向和列方向的压缩，因此增强的2维主成分分析比2维主成分分析需要更少的系数来表示图像，需要更少的存储空间和分类时间。在ORL和FERET人脸库上的实验证明了该方法的有效性。

In this paper, a two-stage method of image feature extraction, called Enhanced two-dimensional principal component analysis （2DPCA）, is proposed in this paper, which uses 2DPCA operated in the row direction and alternative 2DPCA operated in column direction. Enhanced 2DPCA can compress image in row and column direction. Enhanced 2DPCA needs fewer coefficients for image representation than 2DPCA does. The experimental results on the ORL and FERET database show that the Enhanced 2DPCA can work well and surpass two-directional two-dimensional principal component analysis（(2D)2PCA）.