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Modular PCA Face Recognition Based on Weighted Average
| Content Provider | Scilit |
|---|---|
| Author | Han, Chengmao |
| Copyright Year | 2009 |
| Description | This paper presents an improved modular PCA approach, that is, modular PCA algorithm based on weighted average. This algorithm extracts weighted average for every sub-block of every training sample in each type of training sample, and normally operates the corresponding sub-block in training sample using weighted average, then all standardized sub-blocks constitute the overall scatter matrix, and thus the optimal projective matrix is obtained; From the middle value of sub-blocks in training set, and normally projecting sub-blocks of training samples and test samples to the projective matrix, then we can get identified characteristics; At last, use the recent distance classifier to class. The test results in the ORL face database show that the proposed method in identifying performance is superior to ordinary modular PCA approach. |
| ISSN | 19131844 |
| e-ISSN | 19131852 |
| DOI | 10.5539/mas.v3n11p64 |
| Alternate Webpage(s) | http://ccsenet.org/journal/index.php/mas/article/download/4314/3743 |
| Journal | Modern Applied Science |
| Issue Number | 11 |
| Volume Number | 3 |
| Language | English |
| Publisher | Canadian Center of Science and Education |
| Publisher Date | 2009-10-15 |
| Access Restriction | Open |
| Subject Keyword | Industrial Engineering Weighted Average Modular Pca Training Sample Sub Blocks |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
