Discriminant analysis based on kernelized decision boundary for face recognition

Authors:
Baochang Zhang;Xilin Chen;Wen Gao
Affiliations:
Computer School, Harbin Institute of Technology, China;Computer School, Harbin Institute of Technology, China;Computer School, Harbin Institute of Technology, China
Venue:
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Year:
2005

Citing 13
Cited 0

Optimal discriminant plane for a small number of samples and design method of classifier on the plane

Pattern Recognition
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Feature Extraction Based on Decision Boundaries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Kernel Based Fisher Discriminant Analysis

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
SVM-based Nonparametric Discriminant Analysis, An Application to Face Detection

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Enhanced Fisher Linear Discriminant Models for Face Recognition

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
Null space-based kernel fisher discriminant analysis for face recognition

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Nonparametric Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Face Recognition

Handbook of Face Recognition
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vector of the optimal Decision Boundary in terms of the Structure Risk Minimization principle. We also use a simple method to prove a property of Support Vector Machine (SVM) algorithm, which is combined with the optimal Decision Boundary Feature matrix to make our method consistent with the Kernel Fisher method(KFD). Moreover, KDBA is easily used in its applications, and the traditional Decision Boundary Analysis implementations are computationally expensive and sensitive to the size of the problem. Text classification problem is first used to testify the effectiveness of KDBA. Then experiments on the large-scale face database, the CAS-PEAL database, have illustrated its excellent performance compared with some popular face recognition methods such as Eigenface, Fisherface, and KFD.