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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Results Principal component analysis of FTIR Spectroscopy exhibited practical value on reflecting the degree of difference in chemical compositions among different species from the same genera.

结果FTIR的主成分分析在反映同属不同种植物化学组成差异程度上具有应用价值。

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Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.

其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。

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Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.

其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。

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