Authors:
H. Abrishami Moghaddam
1
and
M. Ghayoumi
2
Affiliations:
1
K. N. Toosi University of Technology, Iran, Islamic Republic of
;
2
Islamic Azad University, Science and Research Unit, Iran, Islamic Republic of
Keyword(s):
Feature extraction, Support vector machines, Face recognition, Principal component analysis, Independent components analysis, Linear discriminant analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
In this paper, we present an approach that unifies sub-space feature extraction and support vector classification for face recognition. Linear discriminant, independent component and principal component analyses are used for dimensionality reduction prior to introducing feature vectors to a support vector machine. The performance of the developed methods in reducing classification error and providing better generalization for high dimensional face recognition application is demonstrated.