关键词: 二维主元分析法, 双向二维线性鉴别分析方法, 改进的双向二维线性判别分析方法, 压缩, 投影矩阵

Abstract: To the question that extracted dimension of feature coefficient is great based on the traditional two-dimensional linear discriminant analysis，This paper proposes an Improved Bilateral Two-Dimensional Linear Discriminant Analysis（GB2DLDA）.The within-class scatter matrix and the between-class scatter matrix are compressed from two directions，two Fisher discriminant function are formed with the compressed scatter matrix，and then，face image is projected to the projection matrixes，the feature coefficient is extracted.Experiment shows that the dimension of feature coefficient is less than other methods on the same classification accuracy.Choosing suitable feature vector，classification accuracy is better than other methods.

Key words: Two-Dimensional Principal Component Analysis（2DPCA）, Bilateral Two-Dimensional Linear Discriminant Analysis（B2DLDA）, Improved Bilateral Two-Dimensional Linear Discriminant Analysis（GB2DLDA）, compress, projection matrix