Supplier selection based on hierarchical potential support vector machine
Expert Systems with Applications: An International Journal
Audio-guided video-based face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using PCA and SVM
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
Fast image classification algorithms based on random weights networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In this paper, we first develop a direct Bayesian-based support vector machine (SVM) by combining the Bayesian analysis with the SVM. Unlike traditional SVM-based face recognition methods that require one 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 extrapersonal variation. However, the additional simplicity means that the method has to separate two complex subspaces by one hyperplane thus affecting 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. Finally, we combine the adaptive clustering method with multilevel subspace analysis to further improve the recognition performance. 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