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

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...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition and image analysis 2010-12, Vol.20 (4), p.513-527
Main Authors: Shchegoleva, N. L., Kukharev, G. A.
Format: Article
Language:English
Subjects:
Analysis
Computer Science
Digital
Face
Facial
Identification systems
Image Processing and Computer Vision
Pattern Recognition
Principal component analysis
Principal components analysis
Processing
Recognition
Representation
Representations
Studies
Two dimensional
Understanding of Images
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661810040127