Automatic face recognition by support vector machines

Authors:
Huaqing Li;Shaoyu Wang;Feihu Qi
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Year:
2004

Citing 13
Cited 0

Pairwise classification and support vector machines

Advances in kernel methods
Support vector machine pairwise classifiers with error reduction for image classification

MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines

Machine Learning
Learning Support Vectors for Face Verification and Recognition

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition by Support Vector Machines

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition using independent component analysis and support vector machines

Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Radius margin bounds for support vector machines with the RBF kernel

Neural Computation
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Probability Estimates for Multi-class Classification by Pairwise Coupling

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic face recognition, though being a hard problem, has a wide variety of applications. Support vector machine (SVM), to which model selection plays a key role, is a powerful technique for pattern recognition problems. Recently lots of researches have been done on face recognition by SVMs and satisfying results have been reported. However, as SVMs model selection details were not given, those results might have been overestimated. In this paper, we propose a general framework for investigating automatic face recognition by SVMs, with which different model selection algorithms as well as other important issues can be explored. Preliminary experimental results on the ORL face database show that, with the proposed hybrid model selection algorithm, appropriate SVMs models can be obtained with satisfying recognition performance.