Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting for Fast Face Recognition
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
On solving the face recognition problem with one training sample per subject
Pattern Recognition
Synthetic data generation technique in Signer-independent sign language recognition
Pattern Recognition Letters
Optimization of a training set for more robust face detection
Pattern Recognition
A Random Network Ensemble for Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Actively exploring creation of face space(s) for improved face recognition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Expand training set for face detection by GA re-sampling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition using ada-boosted gabor features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Resampling LDA/QR and PCA+LDA for face recognition
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Designing multiple classifier systems for face recognition
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Coupling adaboost and random subspace for diversified fisher linear discriminant
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to capture all the facial appearance variations. We utilize the resampling techniques to generate several subsets of samples from the original training dataset. A classic appearance-based recognizer, LDA-based classifier, is applied to each of the generated subsets to construct a LDA representation for face recognition. The classification results from each subset are integrated by two strategies: majority voting and the sum rule. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed approaches improve the recognition accuracy of the classical LDA-based face classifier by about 7 percentages.