Face recognition with semi-supervised learning and multiple classifiers
Neamat El Gayar, Shaban A. Shaban, Sayed Hamdy · Computational intelligence · 2006
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.