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[Special EffectsBasedonprincipalcomponentanalysisoftheFaceRecognit

Description: 在特征提取阶段,研究了PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA等多 种方法。不同于基于图象向量的PCA特征提取,由于2DPCA, (2D) ZPCA, DiagPCA和 DiagPCA-I-2DPCA的特征提取都直接基于图象矩阵,计算量小,所以特征的提取速度明 显高于PCA方法。-In the feature extraction stage, the study of the PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA and other methods. Vector is different from the PCA-based image feature extraction, as 2DPCA, (2D) ZPCA, DiagPCA and DiagPCA-I-2DPCA the feature extraction are directly based on image matrix, a small amount of calculation, so the speed of feature extraction method was significantly higher than PCA .
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