Empirical performance analysis of linear discriminant classifiers
- 27 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 164-169
- https://doi.org/10.1109/cvpr.1998.698604
Abstract
In face recognition literature, holistic template matching systems and geometrical local feature based systems have been pursued. In the holistic approach, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are popular ones. More recently, the combination of PCA and LDA has been proposed as a superior alternative over pure PCA and LDA. In this paper, we illustrate the rationales behind these methods and the pros and cons of applying them to pattern classification task. A theoretical performance analysis of LDA suggests applying LDA over the principal components from the original signal space or the subspace. The improved performance of this combined approach is demonstrated through experiments conducted on both simulated data and real data.Keywords
This publication has 8 references indexed in Scilit:
- A feature based approach to face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Manifold caricatures: on the psychological consistency of computer face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Discriminant analysis of principal components for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The FERET evaluation methodology for face-recognition algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Discriminant analysis for recognition of human face imagesJournal of the Optical Society of America A, 1997
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Low-dimensional procedure for the characterization of human facesJournal of the Optical Society of America A, 1987
- Monte Carlo MethodsPublished by Wiley ,1986