Abstract: Aimed at image feature extraction for field application of Principal Component Analysis（PCA） and Two-Dimensional Principal Component Analysis（2DPCA）, this paper uses the eigenvalue to solve the associated two-dimensional sample membership, and applies the relevant eigenvalue classification methods. This method combines two-dimensional eigenvalue, in feature extraction for face reconstruction image, and has advantages of fast stability and local features clear. By introducing the matrix inner product features and classification of 2DPCA to compare the results, the experiments show that, in the ORL and Yale database using the new classification method to identify the method with excellent effects.

摘要： 针对人脸图像特征提取领域应用主成分分析和二维主成分分析方法，使用二维特征值求解相关样本隶属度，并利用相关特征值方法进行分类。该方法结合二维特征值，在特征提取时进行人脸图像重构，具有快速稳定和局部特征清晰的优点。通过引入矩阵内积与二维主成分分析特征分类结果进行比较，实验结果表明，在ORL和Yale数据库中利用该方法进行识别分类取得了很好的效果。