分块二维主成分分析鉴别特征抽取能力研究

基于二维主成分分析(2DPCA),文章提出了分块二维主成分分析(M2DPCA)人脸识别方法。M2DPCA从模式的原始数字图像出发,先对图像进行分块,对分块得到的子图像矩阵采用2DPCA方法进行特征抽取,从而实现模式的分类。新方法的特点是能有效地抽取图像的局部特征,正是这些特征使此类模式区别于彼类。在ORL人脸数据库上测试了该方法的鉴别能力。实验的结果表明,M2DPCA在鉴别性能上优于通常的2DPCA和PCA方法,也优于基于Fisher鉴别准则的鉴别分析方法:Fisherfaces方法、F-S方法和J-Y方法。

The Study of Extracting Ability of Discriminant Features for M2DPCA

Based on Two Dimensional Principal Component Analysis(2DPCA),a new technique called Modular Two Dimensional Principal Component Analysis(M2DPCA) is developed for human face recognition in this paper.First,in proposed approach,the original images are divided into smaller modular images,which are also called sub-images.Then,the well-known 2DPCA method can be directly used to the sub-images obtained from the previous step for feature extraction,so the pattern classification can be implemented on it.The advantage of the presented way when compared with conventional PCA algorithm on original images is that the local discriminant features of the original patterns can be efficiently extracted,which are available to differentiate one class from another.To test M2DPCA and to evaluate its performance,a series of experiments will be performed on ORL human face image databases.The experimental results indicate that the performance of the new method in terms of recognition rate is obviously superior to that of ordinary 2DPCA and PCA algorithms on original images,and is superior to that of some discriminant analysis methods based on the Fisher discriminant criterion such as Fisherfaces and F-S and J-Y methods.