A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Using Hidden Markov Models and Wavelets for Face Recognition
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Face Recognition Committee Machines: Dynamic vs Static Structures
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian face recognition using Gabor features
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Hybrid Face Recognition Method using Markov Random Fields
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Personalized Face Verification System Using Owner-Specific Cluster-Dependent LDA-Subspace
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
IEEE Transactions on Neural Networks
Facial expressions analysis based on cooperative neuro-computing interactions
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Dynamic action recognition based on dynemes and Extreme Learning Machine
Pattern Recognition Letters
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A novel face recognition algorithm that uses dynamic training in a multistage clustering scheme is presented and evaluated. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropy-based measure, that contain the most useful clusters. The best match to the test face is found when only a single face class is retained. This method was tested on the ORL, XM2VTS and FERET face databases, whereas the UMIST database was used in order to train the proposed algorithm. Experimental results indicate that the proposed framework provides a promising solution to the face recognition problem.