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.
Preview
Unable to display preview. Download preview PDF.
References
B. Moghaddam and A. Pentland, āFace Recognition Using View-Based and Modular Eigenspaces for Face Recognitionā, Automatic Systems for the Identification and Inspection of Humans, SPIE vol. 2277. 1994.
X. Jia and M.S. Nixon, āExtending the Feature Vector for Automatic Face Recognitionā, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no 12, pp. 1167ā1176, Dec. 1995.
D. CarreƱo, āSistema de Reconeixement d' Imatges Facials a travĆ©s de Xarxes Neuronals i Algoritmes GenĆØticsā, Bachelor's thesis, La Salle School of Engineering, Barcelona, Spain
Madan M. Gupta and George K. Knopf, Neuro Vision Systems, IEEE Press, 1994
J. Stender, Parallel Genetic Algorithms: Theory and Applications. IOS Press, 1993
W. Lewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, 1989.
C. Torras and G. Wells, āAn Introduction to Neural Networksā, Institut de CibernĆØtica internal report, Univ. PolitĆØcnica de Catalunya, Barcelona, Spain
G.A. Carpenter and S. Grossberg, Neural Networks for Vision and Processing, MIT Press, 1993
J.L.McCLelland, D. E.Rumelhart, Parallel Distributed Processing, āExplorations in the Microstructure of Cognitionā. Vol II: Psychological and Biological models.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
Ā© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
CarreƱo, D., Ginesta, X. (1997). Facial image recognition using neural networks and genetic algorithms. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_169
Download citation
DOI: https://doi.org/10.1007/3-540-63460-6_169
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63460-7
Online ISBN: 978-3-540-69556-1
eBook Packages: Springer Book Archive