Automatic Face Recognition - Methods Improvement and Evaluation

Abstract

This paper deals with Automatic Face Recognition (AFR), which means automatic identification of a person from a digital image. Our work focuses on an application for Czech News Agency that will facilitate to identify a person in a large database of photographs. The main goal of this paper is to propose some modifications and improvements of existing face recognition approaches and to evaluate their results. We assume that about ten labelled images of every person are available. Three approaches are proposed: the first one, Average Eigenfaces, is a modified Eigenfaces method; the second one, SOM with Gaussian mixture model, uses Self Organizing Maps (SOMs) for image reduction in the parametrization step and a Gaussian Mixture Model (GMM) for classification; and in the last one, Re-sampling with a Gaussian mixture model, several resize filters are used for image parametrization and a GMM is also used for classification. All experiments are realized using the ORL database. The recognition rate of the best proposed approach, SOM with Gaussian mixture model, is about 97%, which outperforms the “classic” Eigenfaces, our baseline, by 27% in absolute

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Cite this paper

@inproceedings{Lenc2011AutomaticFR, title={Automatic Face Recognition - Methods Improvement and Evaluation}, author={Ladislav Lenc and Pavel Kr{\'a}l}, booktitle={ICAART}, year={2011} }