基于图像矩阵的广义主分量分析

传统的主分量分析在处理图像识别问题时是基于向量的，且没有充分利用训练样本的类别信息。该文提出了一种直接基于图像矩阵的广义主分量分析方法，该方法能够提取包含在类平均图像中的鉴别信息，与传统的主分量分析相比，具有更强的鉴别力。在ORL标准人脸库上的试验结果表明，所提出的方法不仅识别性能优于传统的主分量分析和Fisher线性鉴别分析，而且极大地提高了特征抽取的速度。

A Generalized Principal Component Analysis Based on Image Matrix

The classical Principal Component Analysis (PCA) for image feature extraction is usually based on vectors, which makes it very time-consuming, and the class information in the training sample has not been utilized fully also. To overcome these two drawbacks of PCA, this paper proposes a novel and efficient PCA method based on original image matrices directly. It can extract the discriminant information included in the class mean images. Hence, the proposed method has better discriminant performance than classical PCA. Experimental results on ORL face database show the proposed method is more powerful and efficient than the classical PCA and Fisher linear discriminant analysis.