Face recognition with semi-supervised learning and multiple classifiers

Neamat El Gayar, Shaban A. Shaban, Sayed Hamdy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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 non-homogenous 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.

Original languageEnglish
Title of host publicationCIMMACS'06: Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
EditorsAntonella Cecchi, Nikos Mastorakis
PublisherWorld Scientific and Engineering Academy and Society
Pages296-301
Number of pages6
ISBN (Print)9789608457560
Publication statusPublished - 20 Nov 2006
Event5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics 2006 - Venice, Italy
Duration: 20 Nov 200622 Nov 2006

Conference

Conference5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics 2006
Abbreviated titleCIMMACS '06
CountryItaly
CityVenice
Period20/11/0622/11/06

Keywords

  • Classifier ensembles
  • Face recognition
  • Learning using labeled and unlabelled data
  • Majority vote
  • Multiple Classifier System
  • Semi-Supervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

Fingerprint Dive into the research topics of 'Face recognition with semi-supervised learning and multiple classifiers'. Together they form a unique fingerprint.

Cite this