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IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
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The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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An SVM classification algorithm with error correction ability applied to face recognition
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Face recognition based on gabor-enhanced manifold learning and SVM
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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In previous work, we proposed the Gabor manifold learning method for feature extraction in face recognition, which combines Gabor filtering with Marginal Fisher Analysis (MFA), and obtained better classification result than conventional subspace analysis methods. In this paper we propose an Enhanced Marginal Fisher Model (EMFM), to improve the performance by selecting eigenvalues in standard MFA procedure, and further combine Gabor filtering and EMFM as Gabor-based Enhanced Marginal Fisher Model (GEMFM) for feature extraction. The GEMFM method has better generalization ability for testing data, and therefore is more capable for the task of feature extraction in face recognition. Then, the GEMFM method is integrated with the error correction SVM classifier to form a new face recognition system. We performed comparative experiments of various face recognition approaches on the ORL, AR and FERET databases. Experimental results show the superiority of the GEMFM features and the new recognition system.