Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Ensembling neural networks: many could be better than all
Artificial Intelligence
Membership authentication in the dynamic group by face classification using SVM ensemble
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Face recognition from a single image per person: A survey
Pattern Recognition
Two-dimensional non-negative matrix factorization for face representation and recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
Local binary LDA for face recognition
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
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This paper introduces a method using the holistic and the local features for face image recognition. The holistic feature is extracted from spatial domain by 2DPCA and the local feature is taken from 2D-DCT-frequency domain by 2DNMF, respectively. 2D-DCT coefficients form the different frequency components and get energy concentrate at the same time, which may be suitable to preserve some useful puny features often ignored in global method. And it may avoid the correlation between global and local features and offer complementary frequency information to spatial one. Finally, LSSVM regression is used to weight the mixed feature vectors and classify images. Experimental results have demonstrated the validity of the new method, which outperforms the conventional 2D-based PCA and NMF methods on ORL and JAFFE face databases.