COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Random Subspaces and Subsampling for 2-D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Active Learning in Face Recognition: Using Tracking to Build a Face Model
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Journal of Cognitive Neuroscience
Assessment of time dependency in face recognition: an initial study
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Resampling for face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
Evolutionary discriminant feature extraction with application to face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
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We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We also construct ensembles of classifiers learned from such actively sampled image sets, which further provides improvement in the recognition rates. We not only signicantly reduce the number of images required in the training set but also improve the accuracy over learning from all the images. We also show that the single face space or ensemble of face spaces, thus constructed, has a higher generalization performance across different illumination and expression conditions.