基于主分量特征与独立分量特征的人脸识别

PCA方法抽取出的主分量特征与ICA方法抽取出的独立分量特征是对原数据的两类不同描述．PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术，PCA方法所抽取特征的各分量之间是统计不相关的．ICA方法使用数据的二阶和高阶信息抽取数据的独立分量特征．文章对这两种方法做了理论上的比较，并通过实验证明ICA算法提取的特征子空间在人脸识别应用中更有效，识别率更高．

Face Recognition Based On Principal Components And Independent Components

The two kinds of features extracted by PCA and ICA represent data are from different points of view. PCA （principal component analysis） is the optimal dimension compression technique based on second--order information, in the sense of mean-square error. Features extracted by PCA are statistically uncorrelated to each other. ICA （independent component analysis） extracts features for data using their second--order and higher--order information. Compared with PCA,the independent components of ICA are both nongaussian and statistically independent. ICA base on higher--order statistics has shown great promising ability in image feature extraction and image compression.