An introduction to wavelets
Pairwise classification and support vector machines
Advances in kernel methods
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Face recognition based on gabor enhanced marginal fisher model and error correction SVM
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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Recently proposed Marginal Fisher Analysis (MFA), as one of the manifold learning methods, has obtained better classification results than the conventional subspace analysis methods and other manifold learning algorithms such as ISOMAP and LLE, because of its ability to find the intrinsic structure of data space and its nature of supervised learning as well In this paper, we first propose a Gabor-based Marginal Fisher Analysis (GMFA) approach for face feature extraction, which combines MFA with Gabor filtering The GMFA method, which is robust to variations of illumination and facial expression, applies the MFA to augmented Gabor feature vectors derived from the Gabor wavelet representation of face images Then, the GMFA method is integrated with the Error Correction SVM classifier to form a novel face recognition system We performed comparative experiments of various face recognition approaches on ORL database and FERET database Experimental results show superiority of the GMFA features and the new recognition system presented in the paper.