The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.

基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。

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A face recognition method based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD) is presented.

提出了奇异值分解(SVD)和主分量分析(PCA)相结合的人脸识别算法。

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A face recognition method based on 2d Principal Component Analysis (2dpca) and compressive sensing is introduced in this paper.

提出一种基于二维主成份分析(2dpca)和压缩感知的人脸识别方法。

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