Method of Numerical Analysis of Similarity and Differences of Face Shape of Twins

  • M. Rychlik
  • W. Stankiewicz
  • M. Morzynski
Part of the IFMBE Proceedings book series (IFMBE, volume 23)

Abstract

This article presents application of the PCA (Principal Component Analysis) method for analysis and computation of three dimensional biometric description of 3D objects. As the data input the geometrical data sets (threedimensional points coordinates) of faces are used.

Authors apply 3D version of PCA method for numerical estimation of similarity and extraction of difference of analyzed faces. PCA decompose set of 3D objects into mean face and individual features (empirical modes), which describing deviations from mean value. Obtained mean shape describe the similarity of faces, eigenmodes present geometrical differences between faces. Eigenvalues can be used to numerical (mathematical) comparison of study faces.

In this paper three sets of data of different type of twins (identical — monozygotic, fraternal-dizygotic) and thirteen typical faces are used and compared. The mean face and the features (eigenfaces) are presented and discussed.

Authors propose using the set of eigenfaces and corresponding coefficient values (computed from PCA) for security verification. As an example of authorization key the set of coefficient values for the faces are presented. Each key describes individual shape of face and can be decoded and compared with the original data of user to obtain access to restricted area or data.

Keywords

3D face recognition biometrics identification Principal Component Analysis (PCA) Reverse Engineering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson R. J. (2008) Security Engineering: A Guide to Building Dependable Distributed Systems, Wiley Publishing Inc., Indiana, USA ISBN-13: 9780470068526.Google Scholar
  2. 2.
    Anil K. J., Prabhakar S., Pankanti S. (2002), On the Similarity of identical twin fingerprints, The Journal of Patter Recognition Society Nr 35, 2653–2663.Google Scholar
  3. 3.
    Mainguet J-F. (2004) Biometrics, URL: http://pagespersoorange. fr/fingerchip/biometrics/biometrics.htm.Google Scholar
  4. 4.
    Miller A. (2000) Risks in Biometric-based Authentication Schemes, GIAC Level One Certification Practical: Internet Research Project, SANS Institute.Google Scholar
  5. 5.
    Guardia A/S (2008) Developer of 3D infrared face recognition system, URL: www.guardia.comGoogle Scholar
  6. 6.
    Biometric Visions (2008), URL: http://www.biometric visi ons.com.Google Scholar
  7. 7.
    Ridley M. (1993) The Red Queen: Sex and the Evolution of Human Nature, New York: Viking Books, ISBN 0-1402-4548-0.Google Scholar
  8. 8.
    Holmes, P., Lumley, J., L., Berkooz, G. (1998), Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge University Press, Cambridge, New Edition.Google Scholar
  9. 9.
    Schneider W. (2007) Report of the Defense Science Board Task Force on Defense Biometrics, Department of Defense United States of America, Washington D.C.Google Scholar
  10. 10.
    Xiaoguang Lu (2006) 3D Face Recognition across Pose and Expression, PhD thesis, Michigan State University.Google Scholar

Copyright information

© International Federation of Medical and Biological Engineering 2009

Authors and Affiliations

  • M. Rychlik
    • 1
  • W. Stankiewicz
    • 1
  • M. Morzynski
    • 1
  1. 1.Department of Combustion Engines and Transportations, Division of Methods of Machine DesignPoznan University of TechnologyPoznanPoland

Personalised recommendations