An experimental evaluation of linear and kernel-based classifiers for face recognition

Authors:
Congde Lu;Taiyi Zhang;Wei Zhang;Guang Yang
Affiliations:
Department of Information & Communication Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Department of Information & Communication Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China;School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR;Department of Communication, Xi'an Institute of Post and Telecommunication, Xi'an Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

Citing 7
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Kernel Nearest-Neighbor Algorithm

Neural Processing Letters
An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition

WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Face Recognition Using Kernel Based Fisher Discriminant Analysis

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A comparison of subspace analysis for face recognition

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the results of a comparative study of linear and kernel-based methods for face recognition. We focus mainly on the experimental comparison of classification methods, i.e. Nearest Neighbor, Linear Support Vector Machine, Kernel based Nearest Neighbor and Nonlinear Support Vector Machine. Some interesting conclusions can be obtained after all of these methods are performed on two wellknown database, i.e. ORL, YALE Face Database, respectively.