关键词: 主成分分析, 二维主成分分析, 特征向量维数, 小样本, 双线性插值, 图像旋转

Abstract: This paper uses PCA and 2DPCA to do the face recognition，and obtains dimensions of eigenvalues and recognition accuracy by using different pre-set thresholds of biggest eigenvalues，and gives the optimalizing dimensions and the threshold of two above methods.Then，using the image rotation technology by bilinear interpolation algorithm to add the numbers of the samples when there is small sample size for same person.It gets higher recognition accuracy and solves the small sample size problem in some degree.If some new predicted information of the small sample images can be generated for same person such as increasing the different expressions and depth of the face for each sample，the effects of the test will be more obvious.

Key words: Principal Component Analysis（PCA）, two Dimensional Principal Component Analysis（2DPCA）, dimension, small samples size, bilinear interpolation, image rotating