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The purpose of this work is to identify a given face image using main features of face. The dimensionality of face image is reduced by the Principal component analysis (PCA, using eigenfaces method) and the recognition is done by the Back propagation
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关于特征提取的文章和代码，基于稀疏化的主成分分析法的，还没运行过，应该不错，共享-Articles and code feature extraction method based on principal component analysis sparse, not run, it should be good, sharing
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2DPCA,即二维主成分分析，相对于传统的PCA（主成分分析），2DPCA在对二维图像进行降维时不需要转成一维（向量）-2DPCA, ie two-dimensional principal component analysis, as opposed to the traditional PCA (Principal Component Analysis), 2DPCA in dimensionality reduction of two-dimensional images into one
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主成分分析 （ Principal Component Analysis ， PCA ）；特征脸；opencv-Principal component analysis (Principal Component Analysis, PCA) characteristic face opencv
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主成分分析法对人脸的识别，识别效果很好，可直接使用-Principal component analysis to identify the human face, and to identify good effect, can be used directly
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基于主成分分析（ICA）的图像压缩与重建-Based on principal component analysis (ICA) image compression and reconstruction
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基于主成分分析法的人脸识别程序的实现，效果很好-Realization of principal component analysis based on face recognition program, the effect is very good
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主成分分析：实现对对维数较大的矩阵进行降维处理- principal component analysis
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