Application of two-dimensional principal component analysis for recognition of face images

被引：10
|
作者：
Shchegoleva N.L. [1 ]
Kukharev G.A. [1 ]
机构：
[1] Saint Petersburg State Electrotechnical University, Saint Petersburg, 197376
来源：
Pattern Recognition and Image Analysis | 2010年 / 20卷 / 04期
关键词：
2D PCA application; Facial images recognition; Parallel and cascade forms of 2D PCA realization; Reduction of attribute space dimension; Reduction of operational complexity; Two-Dimensional Principal Component Analysis (2D PCA);
D O I：
10.1134/S1054661810040127
中图分类号：
O212 [数理统计];
学科分类号：
摘要：
A two-dimensional principal component analysis (2D PCA) method directed at processing digital images is discussed. The method is based on representation of images as a set of rows and columns analyzing these sets. Two methods of realizing the 2D PCA corresponding to the parallel and cascade forms of its realization are presented, and their characteristics are estimated. The application of the 2D PCA method is shown for solving problems of representation and recognition of facial images. The experiments are fulfilled on ORL and FERET bases. © 2010 Pleiades Publishing, Ltd.
引用
收藏
页码：513 / 527
页数：14
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