Tuning Kernel Parameters with Different Gabor Features for Face Recognition

Authors:
Linlin Shen;Zhen Ji;Li Bai
Affiliations:
Faculty of Information and Engineering, ShenZhen University, 518060, China;Faculty of Information and Engineering, ShenZhen University, 518060, China;School of Computer Science and Information Technology, University of Nottingham, Nottingham NG8 1BB, UK
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

Citing 16
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Gender Classification with Support Vector Machines

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Kernel-based nonlinear discriminant analysis for face recognition

Journal of Computer Science and Technology
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
A review on Gabor wavelets for face recognition

Pattern Analysis & Applications
MutualBoost learning for selecting Gabor features for face recognition

Pattern Recognition Letters
Gabor wavelets and General Discriminant Analysis for face identification and verification

Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification

Image and Vision Computing
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Improving kernel Fisher discriminant analysis for face recognition

IEEE Transactions on Circuits and Systems for Video Technology
Face recognition with radial basis function (RBF) neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Kernel methods like support vector machine, kernel principal component analysis and kernel fisher discriminant analysis have recently been successfully applied to solve pattern recognition problems such as face recognition. However, most of the papers present the results without giving kernel parameters, or giving parameters without any explains. In this paper, we present an experiments based approach to optimize the performance of a Gabor feature and kernel method based face recognition system. During the process of parameter tuning, the robustness of the system against variations of kernel function, kernel parameters and Gabor features are extensively tested. The results suggest that the kernel method based approach, with tuned parameters, achieves significantly better results than other algorithms available in literature.