Bayesian face recognition using support vector machine and face clustering

Authors:
Zhifeng Li;Xiaoou Tang
Affiliations:
Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Year:
2004

Citing 9
Cited 8

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The FERET Evaluation Methodology for Face-Recognition Algorithms

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Discriminant Analysis of Principal Components for Face Recognition

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition by Support Vector Machines

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

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Unified Subspace Analysis for Face Recognition

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition using discriminant eigenvectors

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Intelligent control of the hierarchical agglomerative clustering process

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Feature extraction methods for real-time face detection and classification

EURASIP Journal on Applied Signal Processing
Dynamic training using multistage clustering for face recognition

Pattern Recognition
Audio-guided video-based face recognition

IEEE Transactions on Circuits and Systems for Video Technology
Recursive Bayesian linear regression for adaptive classification

IEEE Transactions on Signal Processing
A multi-level supporting scheme for face recognition under partial occlusions and disguise

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Multistage face recognition using adaptive feature selection and classification

ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Recent advances in subspace analysis for face recognition

SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Bayesian face revisited: a joint formulation

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we first develop a direct Bayesian based Support Vector Machine by combining the Bayesian analysis with the SVM. Unlike traditional SVM-based face recognition method that needs to train a large number of SVMs, the direct Bayesian SVM needs only one SVM trained to classify the face difference between intrapersonal variation and extra-personal variation. However, the added simplicity means that the method has to separate two complex subspaces by one hyperplane thus affects the recognition accuracy. In order to improve the recognition performance we develop three more Bayesian based SVMs, including the one-versus-all method, the Hierarchical Agglomerative Clustering based method, and the adaptive clustering method. We show the improvement of the new algorithms over traditional subspace methods through experiments on two face databases, the FERET database and the XM2VTS database.