FACE PATTERN DETECTION - An approach using neural networks

Adriano Martins Moutinho, Antonio Carlos Gay Thomé, Luiz Biondi Neto, Pedro Henrique Gouvêa Coelho

2004

Abstract

Security systems based on face recognition often have to deal with the problem of finding and segmenting the region of the face, containing nose, mouth and eyes, from the rest of the objects in the image. Finding the right position of a face is a part of any automatic identity recognition system, and it is, by itself, a very complex problem to solve, normally being handled separately. This paper describes an approach, using artificial neural networks (ANN), to find the correct position and separate the face from the background. In order to accomplish this goal, a windowing method was created and combined with several image pre-processing steps, from histogram equalization to illumination correction, as an attempt to improve neural network recognition capability. This paper also proposes methods to segment facial features such as mouth, nose and eyes. Finally, the system is tested using 400 images and the performance of face and facial features segmentation is presented.

References

  1. Haykin, S., 1999. Neural Networks, a comprehensive Foundation, 2nd edition. Prentice Hall Press, New Jersey.
  2. Rowley, H., 1999. Neural Network-Based Face Detection. School of Computer Science, Computer Science Department, Carnegie Mellon University Pittsburgh.
  3. Gonzalez, R. C., 1992. Digital Image Processing, Addison-Wesley.
  4. Zurada, J. M., 1992. Introduction to Artificial Neural Systems, West Publishing Company, New York.
  5. AT&T, 2003. AT&T Laboratories, Cambridge University Engineering Department, Cambridge, http://www.uk.research.att.com.
  6. Johnson, R. 1998. Applied Multivariate Statistical Analysis, Prentice-Hall, EUA, 1998.
  7. Demuth, H. and Beale M. 2001. Neural Network Matlab Toolbox, user's guide. MathWorks, Inc.
  8. Warren, S. 2001. Internet Neural Network FAQ.ftp://ftp.sas.com/pub/neural/FAQ.html. Copyright 1997, 1998, 1999, 2000, 2001 by Warren S. Sarle, Cary, NC, USA, 2001.
  9. Mäkinen et al, 2002. Real-Time Face Detection for Kiosk Interfaces. Unit for Computer-Human Interaction (TAUCHI) Department of Computer and Information Sciences FIN-33014 University of Tampere, Finland.
  10. Van Rullen, 2002. FACE PROCESSING USING ONE SPIKE PER NEURONE. Centre de Recherche Cerveau & Cognition, UMR 5549, 133 route de Narbonne, 31062 Toulouse, France.
Download


Paper Citation


in Harvard Style

Martins Moutinho A., Carlos Gay Thomé A., Biondi Neto L. and Henrique Gouvêa Coelho P. (2004). FACE PATTERN DETECTION - An approach using neural networks . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 172-177. DOI: 10.5220/0002610301720177


in Bibtex Style

@conference{iceis04,
author={Adriano Martins Moutinho and Antonio Carlos Gay Thomé and Luiz Biondi Neto and Pedro Henrique Gouvêa Coelho},
title={FACE PATTERN DETECTION - An approach using neural networks},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={172-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002610301720177},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - FACE PATTERN DETECTION - An approach using neural networks
SN - 972-8865-00-7
AU - Martins Moutinho A.
AU - Carlos Gay Thomé A.
AU - Biondi Neto L.
AU - Henrique Gouvêa Coelho P.
PY - 2004
SP - 172
EP - 177
DO - 10.5220/0002610301720177