Random Subspaces and Subsampling for 2-D Face Recognition

Authors:
Nitesh V. Chawla;Kevin W. Bowyer
Affiliations:
University of Notre Dame;University of Notre Dame
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

Citing 0
Cited 8

A linear discriminant analysis framework based on random subspace for face recognition

Pattern Recognition
1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?

Pattern Recognition
Semi-random subspace method for face recognition

Image and Vision Computing
Actively exploring creation of face space(s) for improved face recognition

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Sort-merge feature selection and fusion methods for classification of unstructured video

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multilinear principal component analysis for face recognition with fewer features

Neurocomputing
Coupling adaboost and random subspace for diversified fisher linear discriminant

ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
QUEST hierarchy for hyperspectral face recognition

Advances in Artificial Intelligence - Special issue on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Random subspaces are a popular ensemble construction technique that improves the accuracy of weakclassifiers. It has been shown, in different domains, that random subspaces combined with weak classifiers such as decision trees and nearest neighbor classifiers can provide an improvement in accuracy. In this paper, we apply the random subspace methodology to the 2-D face recognition task. The main goal of the paper is to see if the random subspace methodology can do as well, if not better, than the single classifier constructed on the tuned face space. We also propose the use of a validation set for tuning the face space, to avoid bias in the accuracy estimation. In addition, we also compare the random subspace methodology to an ensemble of subsamples of image data. This work shows that a random subspaces ensemble can outperform a well-tuned single classifier for a typical 2-D face recognition problem. The random subspaces approach has the added advantage of requiring less careful tweaking.