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中文摘要：
提出基于白化散度差矩阵的独立元分析算法,增加不同类表情之间的类间距离,减弱人脸个体差异性信息对表情识别的干扰,避免传统的二维主元分析方法（2DPCA）以总体散布矩阵作为产生矩阵,有效地简化了白化实现过程,提高了白化性能,削弱了光照、姿态等噪声对表情识别的影响。该算法首先采用散度差矩阵求白化矩阵,由快速固定点算法（FASTICA）求解样本独立元,最终由最近邻准则实现表情识别。实验结果表明,提出的算法要优于传统的2DPCA及ICA算法,为表情识别提供了一条有效途径。
英文摘要：
As the traditional ICA does not consider the impornance of the independent components for classification and recognition.This paper proposed a method,which obtained scatter difference matrix by calculation of expression face matrix and neutral face matrix,abolished the total scatter matrix as a generation matrix which had been employed by 2DPCA.As a result,increased difference of between-class scatter,and weakened the noisy from the variety of face.Firstly this method whitened scatter difference matrix.Secondly,calculated independent components by FASTICA.Finally,used a nearest neighbor rule for expression recognition.Experiment result shows that correct recognition rate by the method is higher than that by 2DPCA and traditional ICA,and is valid for expression recognition.
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