Machine Learning
Face Recognition by Combining Several Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Combining classifiers for face recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Using co-training and self-training in semi-supervised multiple classifier systems
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Semi-supervised PCA-Based face recognition using self-training
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
A comparative study of simple online learning strategies for streaming data
WSEAS Transactions on Circuits and Systems
Incremental template updating for face recognition in home environments
Pattern Recognition
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Face recognition using labeled and unlabelled data has received considerable amount of interest in the past years. In the same time, multiple classifier systems (MCS) have been widely successful in various pattern recognition applications such as face recognition. MCS have been very recently investigated in the context of semi-supervised learning. Very few attention has been devoted to verifying the usefulness of the newly developed semi-supervised MCS models for face recognition. In this work we attempt to access and compare the performance of several semi-supervised MCS training algorithms when applied to the face recognition problem. Experiments on a data set of face images are presented. Our experiments use nonhomogenous classifier ensemble, majority voting rule and compare between a three semi-supervised learning models: the self-trained single classifier model, the ensemble driven model and a newly proposed modified co-training model. Experimental results reveal that the investigated semi-supervised models are successful in the exploitation of unlabelled data to enhance the classifier performance and their combined output. The proposed semi-supervised learning model has shown a significant improvement of the classification accuracy compared to existing models.