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Facial image recognition using neural networks and genetic algorithms

  • David Carreño
  • Xavier Ginesta
Poster Session I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)

Abstract

This paper addresses the design of a simple yet efficient facial image recognition system. We show that a face can be recognised based on the relative size and position of its basic features, i.e., eyes, nose and lips. The key to the efficiency of our algorithm is in the feature search method employed. Feature search is accomplished through the combination of conventional template matching and genetic algorithms. Genetic algorithms alone would take a long time in order to converge to a valid solution. However, by first performing a coarse but fast template matching, we can obtain an approximate solution that can be utilised to initialise the genetic algorithm. The output of the facial feature detection stage is fed to a back-propagation neural network which accomplishes the recognition task. Our experimental results show that the system is very efficient both computationally and in recognition accuracy as long as the facial database to be recognised has a moderate size (16 in our experiments). We also note that the basic ideas conveyed in this work can be easily generalised to general purpose object recognition applications.

Category

Active real-time vision and Object recognition 

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References

  1. [1]
    B. Moghaddam and A. Pentland, “Face Recognition Using View-Based and Modular Eigenspaces for Face Recognition”, Automatic Systems for the Identification and Inspection of Humans, SPIE vol. 2277. 1994.Google Scholar
  2. [2]
    X. Jia and M.S. Nixon, “Extending the Feature Vector for Automatic Face Recognition”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no 12, pp. 1167–1176, Dec. 1995.CrossRefGoogle Scholar
  3. [3]
    D. Carreño, “Sistema de Reconeixement d' Imatges Facials a través de Xarxes Neuronals i Algoritmes Genètics”, Bachelor's thesis, La Salle School of Engineering, Barcelona, SpainGoogle Scholar
  4. [4]
    Madan M. Gupta and George K. Knopf, Neuro Vision Systems, IEEE Press, 1994Google Scholar
  5. [5]
    J. Stender, Parallel Genetic Algorithms: Theory and Applications. IOS Press, 1993Google Scholar
  6. [6]
    W. Lewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer VerlagGoogle Scholar
  7. [7]
    D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, 1989.Google Scholar
  8. [8]
    C. Torras and G. Wells, “An Introduction to Neural Networks”, Institut de Cibernètica internal report, Univ. Politècnica de Catalunya, Barcelona, SpainGoogle Scholar
  9. [9]
    G.A. Carpenter and S. Grossberg, Neural Networks for Vision and Processing, MIT Press, 1993Google Scholar
  10. [10]
    J.L.McCLelland, D. E.Rumelhart, Parallel Distributed Processing, “Explorations in the Microstructure of Cognition”. Vol II: Psychological and Biological models.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • David Carreño
    • 1
  • Xavier Ginesta
    • 1
  1. 1.La Salle School of EngineeringBarcelonaSpain

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