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The Art of Artificial Evolution pp 189–210Cite as

Evolving Human Faces

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Part of the Natural Computing Series book series (NCS)

Summary

Witnesses and victims of serious crime are normally requested to construct a picture of the criminal’s face. These pictures are known as facial composites and are typically produced by a witness recalling details of the face and then selecting individual facial features: hair, eyes, nose, mouth, etc. While composites remain an important tool for the apprehension of criminals, research has suggested that, even under favorable conditions, they are rarely recognized. In the current chapter, we present a new method called EvoFIT whereby users select complete faces and a composite is “evolved” using a Genetic Algorithm. While considerable development was required to tune the new approach, research indicates that EvoFIT now produces more identifiable composites than those produced from the traditional “feature” systems. Novel applications of the technology are also discussed.

Keywords

  • Facial Feature
  • Face Model
  • Target Face
  • Facial Shape
  • Face Space

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Stirling, Stirling FK9 4LA, UK

    Charlie D. Frowd

  2. Department of Psychology, University of Stirling, Stirling FK9 4LA, UK

    Peter J. B. Hancock

Authors
  1. Charlie D. Frowd
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  2. Peter J. B. Hancock
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Technologies, University of A Coruña Campus de Elvina, 15071 La Coruña, Spain

    Juan Romero

  2. CISUC Dept. of Informatics Engineering, University of Coimbra, 3030 Coimbra, Portugal

    Penousal Machado

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© 2008 Springer-Verlag Berlin Heidelberg

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Frowd, C.D., Hancock, P.J.B. (2008). Evolving Human Faces. In: Romero, J., Machado, P. (eds) The Art of Artificial Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72877-1_9

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  • DOI: https://doi.org/10.1007/978-3-540-72877-1_9

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