The Study of Extracting Ability of Discriminant Features for Modular PCA
分块PCA鉴别特征抽取能力的分析研究

Based on Principal Component Analysis(PCA),a new technique called Modular PCA is developed for human face recognition in this paper.First,in proposed approach,the original images are divided into smaller modular ima- ges,which are also called sub-images.Then,the well-known PCA method can be directly used to the sub-images ob- tained from the previous step for feature extraction,so the pattern classification can be implemented.The advantage of the represented 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 Modular PCA and to evaluate its performance,a series of experiments were performed on Yale human face im- age databases.The experimental results indicate that the performance of the new method in terms of recognition rate is obviously superior to that of ordinary PCA algorithm on original images,and is superior to that of some discriminant a- nalysis based on the Fisher discriminant criterion such as Fisherfaces,F-S and combination method.