Paper Info
Title
Two-Dimensional Weighted Pca Algorithm For Face Recognition
Abstract
Principle Component Analysis (PCA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in PCA the image always needs to be transformed into ID vector, however recently two-dimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into ID vector. In this paper, we propose a new 2DPCA-based method that can improve the performance of the 2DPCA approach. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. Both PCA and 2DPCA may fail to accomplish this, no matter how easy the task is, as they are unsupervised techniques. The directions that maximize the scatter of the data might not be as adequate to discriminate between clusters. So we proposed a new 2DPCA-based scheme which can straightforwardly take into consideration data labeling, and makes the performance of recognition system better. Experiment results show our method achieves better performance in comparison with the 2DPCA approach with the complexity nearly as same as that of 2DPCA method.
Year
DOI
Venue
2005
10.1109/CIRA.2005.1554280
2005 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, PROCEEDINGS
Keywords
Field
DocType
principle component analysis (PCA), two-dimensional PCA (2DPCA), two-dimensional weighted PCA, face recognition
Computer science,Data labeling,Unsupervised learning,Artificial intelligence,Face detection,Kernel (linear algebra),Facial recognition system,Computer vision,Pattern recognition,Independent component analysis,Covariance matrix,Machine learning,Principal component analysis
Conference
Citations 
PageRank 
References 
2
0.40
11
Authors
2
Name
Order
Citations
PageRank
Nhat Minh Dinh Vo1336.05
Sungyoung Lee22932279.41