基于PCA算法的人脸识别

利用PCA算法提供了一个高维和低维间的线性变换矩阵，这个变换矩阵可以通过求取协方差矩阵的特征向量获得而无需其它参数。PCA可逆的线性变换矩阵对截断的误差在均方差意义下最小的特点，即以各维特征欧氏距离上的重建误差和最小为目标，平均对待每一维特征，来截取图像一部分能量，以降低获取数据的维数，提高识别的速度，同时尽可能地提高精度。

Identification of Face Based on PCA Algorithm

Using specialty of principle components analysis(PCA) algorithm provide linearity transformation matrix between hypsi-dimension and hyp-dimension,which should obtain by covariance performance vector and nothing parameter,PCA reversible linearity matrix interceptive error minimum on mean square error(MSE),that is to say,it is a target that use each dimension's characteristic Euler distance re-establishment error and minimum error,average all characteristic vector of each dimension,intercepting some energy of images,in order to reduce demension of capturing data and improve rate and accuracy of identification.