Progress in Artificial Intelligence

, Volume 6, Issue 4, pp 285–298 | Cite as

Improved image registration in skull–face overlay using expert knowledge

  • Oscar GómezEmail author
  • Oscar Ibáñez
  • Oscar Cordón
Regular Paper


Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of the face of an individual and the analysis of their morphological correspondence. This research focused on the skull–face overlay stage with the aim of modeling the expert knowledge that is related to the existing anthropometric differences among landmarks and incorporating it into this stage. Consequently, we have moved from a single-objective optimization problem to a multiobjective optimization one aimed to reduce the distances between pairs of landmarks from each group independently. To tackle it, two classic approaches from the area of multicriteria decision making were used: weighted sum and lexicographical order. The results, which were obtained over a ground truth dataset, are promising in those cases where the forensic expert has located a large number of landmarks, and worse results than the state-of-the-art method in cases with few landmarks.


Computer vision Soft computing Craniofacial superimposition Skull–face overlay Craniometric and cephalometric landmarks 



This research was supported by the Spanish Ministerio de Economía y Competividad under NEWSOCO Project TIN2015-67661-P, and the Andalusian Dept. of Innovación, Ciencia y Empresa under Project TIC2011-7745, including European Development Regional Funds (EDRF). Mr. O. Gomez’s work was supported by Spanish MECD FPU Grant FPU14/02380. Dr. Ibáñz’s work has been supported by Spanish MINECO Juan de la Cierva-Incorporación Fellowship JCI- 2014-22433.


  1. 1.
    Yoshino, M.: Craniofacial superimposition. In: Wilkinson, C., Rynn, C. (eds.) Craniofacial Identification, pp. 238–253. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  2. 2.
    Campomanes-Álvarez, C., Ibáñez, O., Cordón, O.: Design of criteria to assess craniofacial correspondence in forensic identification based on computer vision and fuzzy integrals. Appl. Soft Comput. 46, 596–612 (2016)CrossRefGoogle Scholar
  3. 3.
    Damas, S., Cordón, O., Ibáñez, O., Santamaría, J., Alemán, I., Botella, M.: Forensic identification by computer-aided craniofacial superimposition: a survey. ACM Comput. Surv. 43, 27 (2011)CrossRefGoogle Scholar
  4. 4.
    Damas, S., Wilkinson, C., Kahana, T., Veselovskaya, E., Abramov, A., Jankauskas, R., Jayaprakash, P.T., Ruiz, E., Navarro, F., Huete, M.I., Cunha, E., Cavalli, F., Clement, J., Lestón, P., Molinero, F., Briers, T., Viegas, F., Imaizumi, K., Humpire, D., Ibáñez, O.: Study on the performance of different craniofacial superimposition approaches (ii): best practices proposal. Forensic Sci. Int. 257, 504–508 (2015)CrossRefGoogle Scholar
  5. 5.
    Zitovà, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)CrossRefGoogle Scholar
  6. 6.
    Santamaría, J., Cordón, O., Damas, S.: A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling. Comput. Vis. Image Underst. 115, 1340–1354 (2010)CrossRefGoogle Scholar
  7. 7.
    Damas, S., Cordón, O., Santamaría, J.: Medical image registration using evolutionary computation: an experimental study. IEEE Comput. Intell. Mag. 6, 26–42 (2011)CrossRefGoogle Scholar
  8. 8.
    Chankong, V., Haimes, Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science Ltd, North-Holland (1983)Google Scholar
  9. 9.
    Campomanes-Alvarez, B., Ibanez, O., Campomanes-Alvarez, C., Damas, S., Cordon, O.: Modeling facial soft tissue thickness for automatic skull–face overlay. IEEE Trans. Inf. Forensics Secur. 10(10), 2057–2070 (2015)CrossRefGoogle Scholar
  10. 10.
    Ibáñez, O., Cavalli, F., Campomanes-Álvarez, B.R., Campomanes-Álvarez, C., Valsecchi, A., Huete, M.I.: Ground truth data generation for skull-face overlay. Int. J. Legal Med. 129, 569–581 (2015)CrossRefGoogle Scholar
  11. 11.
    Huete, M.I., Ibáñez, O., Wilkinson, C., Kahana, T.: Past, present, and future of craniofacial superimposition: literature and international surveys. Legal Med. 17(4), 267–278 (2015)CrossRefGoogle Scholar
  12. 12.
    Ghosh, A.K., Sinha, P.: An economised craniofacial identification system. Forensic Sci. Int. 117, 109–119 (2001)CrossRefGoogle Scholar
  13. 13.
    Nickerson, B.A., Fitzhorn, P.A., Koch, S.K., Charney, M.: A methodology for near-optimal computational superimposition of two-dimensional digital facial photographs and three-dimensional cranial surface meshes. J. Forensic Sci. 36, 480–500 (1991)CrossRefGoogle Scholar
  14. 14.
    Ibáñez, O., Ballerini, L., Cordón, O., Damas, S., Santamaría, J.: An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification. Inf. Sci. 79, 3998–4028 (2009)CrossRefGoogle Scholar
  15. 15.
    Ibáñez, O., Cordón, O., Damas, S., Santamaría, J.: Modeling the skull-face overlay uncertainty using fuzzy sets. IEEE Trans. Fuzzy Syst. 16, 946–959 (2011)Google Scholar
  16. 16.
    Ibáñez, O., Cordón, O., Damas, S.: A cooperative coevolutionary approach dealing with the skull–face overlay uncertainty in forensic identification by craniofacial superimposition. Soft Comput. 18, 797–808 (2012)CrossRefGoogle Scholar
  17. 17.
    Martin, R.: Lehrbuch der Anthropologie in systematischer Darstellung mit besonderer Berücksichtigung der anthropologischen Methoden, vol. 4. G. Fischer (1966)Google Scholar
  18. 18.
    George, R.M.: Anatomical and artistic guidelines for forensic facial reconstruction. In: Iscan, M.Y., Helmer, R. (eds.) Forensic Analysis of the Skull, pp. 215–227. Wiley Liss, New York (1993)Google Scholar
  19. 19.
    Faugeras, O.: Three-Dimensional Computer Vision. A Geometric Viewpoint, 1st edn. The MIT Press, Cambridge (1993)Google Scholar
  20. 20.
    Cummaudo, M., Guerzoni, M., Marasciuolo, L., Gibelli, D., Cigada, A., Obertová, Z., Ratnayake, M., Poppa, P., Gabriel, P., Ritz-Timme, S., Cattaneo, C.: Pitfalls at the root of facial assessment on photographs: a quantitative study of accuracy in positioning facial landmarks. Int. J. Legal Med. 127, 699–706 (2013)CrossRefGoogle Scholar
  21. 21.
    Bookstein, F.L.: Morphometric Tools for Landmark Data: Geometry and Biology, 1st edn. Cambridge University Press, New York (1991)zbMATHGoogle Scholar
  22. 22.
    Campomanes-Álvarez, C., Campomanes-Álvarez, B.R., Ibáñez, O., Cordón, O., Guadarrama, S.: An experimental study on fuzzy distances for skull-face overlay in craniofacial superimposition. Fuzzy Sets Syst. 318, 100–119 (2017)Google Scholar
  23. 23.
    Cramon-Taubadel, N.V., Frazier, B.C., Lahr, M.M.: The problem of assessing landmark error in geometric morphometrics: theory, methods, and modifications. Am. J. Phys. Anthropol. 134(1), 24–35 (2007)CrossRefGoogle Scholar
  24. 24.
    Campomanes-Álvarez, B., Ibáñez, O., Navarro, F., Alemán, I., Cordón, O., Damas, S.: Dispersion assessment in the location of facial landmarks on photographs. Int. J. Legal Med. 129(1), 227–236 (2015)CrossRefGoogle Scholar
  25. 25.
    Ibáñez, O., Valsecchi, A., Cavalli, F., Huete, M.I., Campomanes-Alvarez, B.R., Campomanes-Alvarez, C., Vicente, R., Navega, D.S., Ross, A., Wilkinson, C., Jankauskas, R., Imaizumi, K., Hardiman, R., Jayaprakash, P.T., Ruiz, E., Molinero, F., Lestón, P., Veselovskaya, E., Abramov, A., Steyn, M., Cardoso, J., Humpire, D., Lusnig, L., Gibelli, D.M., Mazzarelli, D., Gaudio, D., Collini, F., Damas, S.: Study on the criteria for assessing skull-face correspondence in craniofacial superimposition. Legal Med. 23, 59–70 (2016)CrossRefGoogle Scholar
  26. 26.
    Ibáñez, O., Vicente, R., Navega, D.S., Campomanes-Alvarez, B.R., Cattaneo, C., Jankauskas, R., Huete, M.I., Navarro, F., Hardiman, R., Ruiz, E., Imaizumi, K., Cavalli, F., Veselovskaya, E., Humpire, D., Cardoso, J., Collini, F., Mazzarelli, D., Gibelli, D., Damas, S.: Meprocs framework for craniofacial superimposition: validation study. Legal Med. 23, 99–108 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations