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。

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.

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并在特征提取环节，提出CSVD算法与非负矩阵因子算法特征数据相融合的人脸识别算法。

And in the feature extraction process, a new face recognition method based on CSVD and non Negative Matrix Factorization (NMF) is presented.

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眼角的自动定位能够给后续的人脸特征自动提取和识别算法研究奠定良好的基础，帮助提高人脸识别算法的识别率。

This approach would help to extract the vital feature points on human face automatically and improve the accuracy of face recognition.

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眼角的自动定位能够给后续的人脸特征自动提取和识别算法研究奠定良好的基础，帮助提高人脸识别算法的识别率。

This approach would help to extract the vital feature points on human face automatically and improve the accuracy of face recognition.

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