Efficient feature reduction algorithm based on mPCA and rough set

被引：0

作者：

Jin, Yanfeng ^{[1
]}

Geng, Keming ^{[1
]}

Wang, Yongping ^{[1
]}

Zhao, Baozhu ^{[1
]}

机构：

[1] Shi Jiazhuang Post and Telecommunication Technical College, China

来源：

International Journal of Advancements in Computing Technology | 2012年 / 4卷 / 15期

关键词：

Face recognition; Feature reduction algorithm; Rough set;

D O I：

10.4156/ijact.vol4.issue15.59

中图分类号：

学科分类号：

摘要：

To improve the face recognition rate and reduce the complexity of the calculation, an efficient feature reduction algorithm based on modular principal component analysis (mPCA) and rough set. To cope with the variations of pose, lighting direction and facial expression, first divided the face images into smaller sub-images, and then used PCA method to deal with them. To guarantee that the selected first principal components as a feature vector, using rough sets to simplified the features of the processed sub- images by mPCA. At the same time, get rid of the extra, not related and redundant data, to reduce the complexity of the time and space. Then the results indicate high improvement in the classification performance compared to the conventional method. The innovation point of the paper is the perfect combination of the mPCA methods and rough sets. The experiment results show the validity of this method.

引用

页码：504 / 511

页数：7

共 18 条

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