AUTOMATIC FACE RECOGNITION - Methods Improvement and Evaluation

Ladislav Lenc, Pavel Král

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 value.

References

  1. Bledsoe, W. W. (1966). Man-machine facial recognition. Technical report, Panoramic Research Inc., Palo Alto, CA.
  2. Bolme, D. S. (2003). Elastic Bunch Graph Matching. PhD thesis, Colorado State University.
  3. Gross, R. (2005). Face Databases. Springer-Verlag.
  4. Kanade, T. (1977). Computer recognition of human faces. Birkhauser Verlag.
  5. Kepenekci, B. (2001). Wavelet Transform. Technical University.
  6. Lawrence, S., Giles, S., Tsoi, A., and Back, A. (1997). Face recognition: A convolutional neural network approach. IEEE Trans. on Neural Networks.
  7. Sirovich, L. and Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 4.
  8. Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. Computer Vision and Pattern Recognition.
  9. Wiskott, L., Fellous, J.-M., Krüger, N., and von der Malsburg, C. (1999). Face recognition by elastic bunch graph matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition.
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Paper Citation


in Harvard Style

Lenc L. and Král P. (2011). AUTOMATIC FACE RECOGNITION - Methods Improvement and Evaluation . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 604-608. DOI: 10.5220/0003182206040608


in Bibtex Style

@conference{icaart11,
author={Ladislav Lenc and Pavel Král},
title={AUTOMATIC FACE RECOGNITION - Methods Improvement and Evaluation},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={604-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003182206040608},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - AUTOMATIC FACE RECOGNITION - Methods Improvement and Evaluation
SN - 978-989-8425-40-9
AU - Lenc L.
AU - Král P.
PY - 2011
SP - 604
EP - 608
DO - 10.5220/0003182206040608