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中文摘要：
鲁棒主分量分析（RPCA）模型在选取幅度参数时，忽略了各变量独有的统计特性．为克服RPCA模型的这一不足，本文提出了通用鲁棒主分量分析（GRPCA）模型，采用M估计器（M—Estimator）为每个变量估计符合其自身统计特性的幅度参数，以提高模型的鲁棒性和通用性，并在此基础上提出了一种集成小波分解、鲁棒估计及独立分量分析的WR-ICA人脸识别算法．WR-ICA对人脸识别中的多种外部干扰（残缺人脸图像、化妆及遮挡等）都表现出很好的鲁棒性．理论分析和实验结果证实了WR-ICA的有效性，采用Cos距离作相似性度量时，WR-ICA的平均识别率达到99．44％．
英文摘要：
In the robust principal component analysis（RPCA）model, the statistical properties of every variable are neglected when scale parameters are chosen for them. In order to overcome this drawback, a generalized robust principal component analysis （GRPCA）model was proposed in this paper, a M-estimator was adopted to estimate robust scale parameters for every variable according to their statistical properties. Then, a new independent component analysis algorithm for face recognition based on wavelet decomposition and robust estimation （WR-ICA） was proposed. WR-ICA is robust to many types of outliers （incomplete face image,making up,occlusion,etc）.The validity of WR-ICA is confirmed by theory analysis and experimental data, with Cos distance as similarity measurement, the average recognition rate of WR-ICA is 99.44%.
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