Advertisement

Computer-aided superimposition via reconstructing and matching 3D faces to 3D skulls for forensic craniofacial identifications

  • Joi San TanEmail author
  • Iman Yi Liao
  • Ibrahim Venkat
  • Bahari Belaton
  • P. T. Jayaprakash
Original Article
  • 26 Downloads

Abstract

Identification of human remains via craniofacial superimposition (CS) is one of the prominent research areas in the forensic sciences. CS makes use of imaging techniques to identify an unknown skull by matching it with the available face photographs of missing individuals. Life-size enlargement of the face image and orientating the skull to correspond to the posture seen in the face photograph are the two main problems that affect identification accuracy with both the conventional and the computer-aided methods. Unlike the existing techniques, this research proposes a 3D skull–3D face model superimposition (3D–3D) approach to address the above two issues. The proposed method commences by reconstructing the 3D face model from a given 2D face image using the mean simplified generic elastic model, followed by registering the face model to a 3D skull along the jaw line using the analytical curvature B-spline (AC B-spline). The accuracy index of the registration is then evaluated to suggest the degree to which the face image corresponds to a skull. The superimpositions of positive and negative cases were conducted on a set of 3D skulls versus a set of 2D face images. The accuracy indices of the registration results suggest that the AC B-spline is more robust in 3D–3D superimposition compared to the other existing methods. The full experimental results have demonstrated the potential of the proposed method as an assistive tool to the forensic scientists for craniofacial identifications.

Keywords

Craniofacial superimposition 3D face reconstruction Generic elastic model Curve registration B-spline 

Notes

Acknowledgements

This research is supported by Department of Radiology Hospital Universiti Sains Malaysia (RUT Grant, 1001/PPSG/852004).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

CT head scans of patients were randomly collected from the picture archiving and communication system (PACS) server at the Radiology Department, Hospital USM. They were scanned using the Siemens Somatom Definition AS+ 128-slice (Siemens Medical Solutions, Erlangen, Germany). Ethical application was approved by the Ethics and Research Committee USM, reference number USMKK/PPP/JEPeM (246.3[13]). Part of the collected data were also approved by the Research Ethics Committee The National University of Malaysia (UKM PPI/111/8/JEP-2016-100). Besides, this research was supported by the ScienceFund Malaysia (01-01-05-SF0045) and RUI Grant of Universiti Sains Malaysia (1001/PKOMP/814109).

References

  1. 1.
    Austin-Smith, D., Maples, W.R.: The reliability of skull/photograph superimposition in individual identification. J. Forensic Sci. 39(2), 446–455 (1994)CrossRefGoogle Scholar
  2. 2.
    Ballerini, L., Cordón, O., Santamaría, J., Damas, S., Alemán, I., Botella, M.: Craniofacial superimposition in forensic identification using genetic algorithms. In: Proceedings of the Third International Symposium on Information Assurance and Security, pp. 429–434. IEEE Computer Society (2007)Google Scholar
  3. 3.
    Basauri, C.: A body identified by forensic odontology and superimposed photographs. In: International Criminal Police Review, pp. 37–43 (1967)Google Scholar
  4. 4.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194. ACM Press/Addison-Wesley Publishing Co. (1999)Google Scholar
  5. 5.
    Campomanes, A.B.R., Ibáñez, O., Navarro, F., Alemán, I., Botella, M., Damas, S., Cordón, O.: Computer vision and soft computing for automatic skull-face overlay in craniofacial superimposition. Forensic Sci. Int. 245, 77–86 (2014)CrossRefGoogle Scholar
  6. 6.
    Campomanes, A.C., Campomanes, A.B.R., Guadarrama, S., Ibáñez, O., Cordón, O.: An experimental study on fuzzy distances for skull-face overlay in craniofacial superimposition. Fuzzy Sets Syst. 318, 1–20 (2016)MathSciNetGoogle Scholar
  7. 7.
    Campomanes-Alvarez, B., Ibánez, O., Campomanes-Alvarez, C., Damas, S., Cordón, O.: Modeling facial soft tissue thickness for automatic skull-face overlay. IEEE Trans. Inf. Forensics Secur. 10(10), 2057–2070 (2015)CrossRefGoogle Scholar
  8. 8.
    Campomanes-Alvarez, C., Ibáñez, O., Cordón, O., Wilkinson, C.: Hierarchical information fusion for decision making in craniofacial superimposition. Inf. Fusion 39, 25–40 (2018)CrossRefGoogle Scholar
  9. 9.
    Campomanes-Alvarez, C., Ibánez, 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 (2015)CrossRefGoogle Scholar
  10. 10.
    Campomanes-Alvarez, C., Ibánez, O., Cordón, O.: Modeling the consistency between the bony and facial chin outline in craniofacial superimposition. In: 16th World Congress of the International Fuzzy Systems Association (IFSA), pp. 1612–1619 (2015)Google Scholar
  11. 11.
    Clement, J.G., Ranson, D.F.: Craniofacial Identification in Forensic Medicine/Edited by John G. Clement. Arnold, London (1998)Google Scholar
  12. 12.
    Dong, Y., Huang, L., Feng, Z., Bai, S., Wu, G., Zhao, Y.: Influence of sex and body mass index on facial soft tissue thickness measurements of the northern Chinese adult population. Forensic Sci. Int. 222(1–3), 396.e1–7 (2012)Google Scholar
  13. 13.
    Dorion, R.B.: Photographic superimposition. J. Forensic Sci. 28(3), 724–734 (1983)CrossRefGoogle Scholar
  14. 14.
    Fenton, T.W., Heard, A.N., Sauer, N.J.: Skull-photo superimposition and border deaths: identification through exclusion and the failure to exclude. J. Forensic Sci. 53(1), 34–40 (2008)CrossRefGoogle Scholar
  15. 15.
    Glaister, J., Brash, J.C.: Medico-Legal Aspects of the Ruxton Case, vol. 33. Livingstone (1937)Google Scholar
  16. 16.
    Gómez, O., Ibáñez, O., Cordón, O.: Improved image registration in skull-face overlay using expert knowledge. Prog. Artif. Intell. 4, 1–14 (2017)Google Scholar
  17. 17.
    Hagemeier, H.: Identification of a skull by electronic superimposition of images. Int. Crim. Police Rev. 38, 286–90 (1983)Google Scholar
  18. 18.
    Helmer, R., Grüner, O.: Improved skull identification using the superprojection technique with the help of a video-tape system. J. Legal Med. 80(3), 183–187 (1977)Google Scholar
  19. 19.
    Hermann, S., Klette, R.: A comparative study on 2D curvature estimators. Technical report (2006)Google Scholar
  20. 20.
    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. 179(23), 3998–4028 (2009a)CrossRefGoogle Scholar
  21. 21.
    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. 16(5), 797–808 (2012)CrossRefGoogle Scholar
  22. 22.
    Ibáñez, O., Cordón, O., Damas, S., Guadarrama, S., Santamaría, J.: A new approach to fuzzy location of cephalometric landmarks in craniofacial superimposition. In: IFSA/EUSFLAT Conference, pp. 195–200 (2009b)Google Scholar
  23. 23.
    Ibáñez, O., Cordón, O., Damas, S., Santamaría, J.: Craniofacial superimposition based on genetic algorithms and fuzzy location of cephalometric landmarks. In: Hybrid artificial intelligence systems, pp. 599–607. Springer Berlin (2008)Google Scholar
  24. 24.
    Ibáñez, O., Cordón, O., Damas, S., Santamaría, J.: Multimodal genetic algorithms for craniofacial superimposition. In: Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, pp. 119–142 (2009c)Google Scholar
  25. 25.
    Ibáñez, O., Cordón, O., Damas, S., Santamaría, J.: Modeling the skull-face overlay uncertainty using fuzzy sets. IEEE Trans. Fuzzy Syst. 19(5), 946–959 (2011)CrossRefGoogle Scholar
  26. 26.
    Iten, P.X.: Identification of skulls by video superimposition. J. Forensic Sci. 32(1), 173–188 (1987)CrossRefGoogle Scholar
  27. 27.
    Jain, V., Mukherjee, A.: The Indian face database. http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/ (2002). Accessed July 2011
  28. 28.
    Jayaprakash, P.T., Srinivasan, G.J., Amravaneswaran, M.G.: Cranio-facial morphanalysis: a new method for enhancing reliability while identifying skulls by photo superimposition. Forensic Sci. Int. 117(1–2), 121–143 (2001)CrossRefGoogle Scholar
  29. 29.
    Jiang, L., Zhang, J., Deng, B., Li, H., Liu, L.: 3D face reconstruction with geometry details from a single image. IEEE Trans. Image Process. 27(10), 4756–4770 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Kemelmacher-Shlizerman, I., Basri, R.: 3D face reconstruction from a single image using a single reference face shape. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 394–405 (2011)CrossRefGoogle Scholar
  31. 31.
    Li, T., Bolkart, T., Black, M.J., Li, H., Romero, J.: Learning a model of facial shape and expression from 4D scans. ACM Trans. Graph. (TOG) 36(6), 194:1–194:17 (2017)Google Scholar
  32. 32.
    Liu, C., Li, X.: Superimposition-guided facial reconstruction from skull. arXiv preprint arXiv:1810.00107 (2018)
  33. 33.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)CrossRefGoogle Scholar
  34. 34.
    Ma, M., Peng, S., Hu, X.: A lighting robust fitting approach of 3D morphable model for face reconstruction. Vis. Comput. 32(10), 1223–1238 (2016)CrossRefGoogle Scholar
  35. 35.
    Maat, G.J.R.: The positioning and magnification of faces and skulls for photographic superimposition. Forensic Sci. Int. 41(3), 225–235 (1989)CrossRefGoogle Scholar
  36. 36.
    Maghari, A., Venkat, I., Liao, I.Y., Belaton, B.: PCA-based reconstruction of 3D face shapes using Tikhonov regularization. Int. J. Adv. Soft Comput. Appl. 5(2), 1–15 (2013)Google Scholar
  37. 37.
    Maghari, A., Venkat, I., Liao, I.Y., Belaton, B.: Adaptive face modelling for reconstructing 3D face shapes from single 2D images. Comput. Vis. IET 8(5), 441–454 (2014)CrossRefGoogle Scholar
  38. 38.
    Maghari, A.Y., Liao, I.Y., Venkat, I., Belaton, B.: Distance-based 3D face reconstruction using regularization. In: Zaman, H.B., Robinson, P., Smeaton, A.F., Shih, T.K., Velastin, S., Jaafar, A., Ali, N.M. (eds.) Advances in Visual Informatics, pp. 476–493. Springer, Berlin (2015)CrossRefGoogle Scholar
  39. 39.
    Manhein, M.H., Listi, G.A., Barsley, R.E., Musselman, R., Barrow, N.E., Ubelaker, D.H.: In vivo facial tissue depth measurements for children and adults. J. Forensic Sci. 45(1), 48–60 (2000)CrossRefGoogle Scholar
  40. 40.
    McKenna, J.J.I.: A method of orientation of skull and camera for use in forensic photographic investigation. J. Forensic Sci. 33(3), 751–755 (1988)MathSciNetCrossRefGoogle Scholar
  41. 41.
    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(2), 480–500 (1991)CrossRefGoogle Scholar
  42. 42.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: CVPR, vol. 1, pp. 947–954. IEEE (2005)Google Scholar
  43. 43.
    Sahni, D., Singh, G., Jit, I., Singh, P.: Facial soft tissue thickness in northwest Indian adults. Forensic Sci. Int. 176(2), 137–146 (2008)CrossRefGoogle Scholar
  44. 44.
    Santamaría, J., Cordón, O., Damas, S., Ibáñez, O.: 3D-2D image registration for craniofacial superimposition in forensic medicine using covariance matrix adaptation evolution strategy. In: 9th International conference on information technology and applications in biomedicine, pp. 1–4 (2009)Google Scholar
  45. 45.
    Schroeder, W.J.: The Visualization Toolkit User’s Guide: Updated for Version 4.0. Kitware (1998)Google Scholar
  46. 46.
    Sekharan, C.: Positioning the Skull for Superimposition. Wiley, New York (1993)Google Scholar
  47. 47.
    Sekharan, P.C.: A revised superimposition technique for identification of the individual from the skull and photograph. J. Crim. Law Criminol. Police Sci. 28, 107–113 (1971)CrossRefGoogle Scholar
  48. 48.
    Sekharan, P.C.: A scientific method for positioning of the skull for photography in superimposition studies. J. Police Sci. Adm. 1, 232–240 (1973)Google Scholar
  49. 49.
    Sen, N.K.: Identification by superimposed photographs. In: International Criminal Police Review, pp. 284–286 (1962)Google Scholar
  50. 50.
    Sipahioǧlu, S., Ulubay, H., Diren, H.B.: Midline facial soft tissue thickness database of Turkish population: MRI study. Forensic Sci. Int. 219(1), 282.e1–282.e8 (2012)Google Scholar
  51. 51.
    Smith, W.A., Hancock, E.R.: Coupled statistical face reconstruction. In: Gagalowicz, A., Philips, W. (eds.) Computer Analysis of Images and Patterns, pp. 153–161. Springer, Berlin (2005)CrossRefGoogle Scholar
  52. 52.
    Tan, J.S., Venkat, I., Belaton, B.: An analytical curvature B-spline algorithm for effective curve modeling. In: International Visual Informatics Conference, pp. 283–295. Springer (2015)Google Scholar
  53. 53.
    Tan, J.S., Venkat, I., Jayaprakash, P.: Computer-aided craniofacial superimposition using a quasi-Newton iterative closest point approach. Scienceasia 42, 136–145 (2016)CrossRefGoogle Scholar
  54. 54.
    Tan, J.S., Venkat, I., Liao, I.Y., De Wilde, P.: General human traits oriented generic elastic model for 3d face reconstruction. In: BMVC, pp. 1–13 (2016)Google Scholar
  55. 55.
    Yoshino, M., Imaizumi, K., Miyasaka, S., Seta, S.: Evaluation of anatomical consistency in craniofacial superimposition images. Forensic Sci. Int. 74(1–2), 125–134 (1995)CrossRefGoogle Scholar
  56. 56.
    Yoshino, M., Matsuda, H., Kubota, S., Imaizumi, K., Miyasaka, S., Seta, S.: Computer-assisted skull identification system using video superimposition. Forensic Sci. Int. 90(3), 231–244 (1997)CrossRefGoogle Scholar
  57. 57.
    Zhang, R., Tsai, P.S., Cryer, J.E., Shah, M.: Shape-from-shading: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 690–706 (1999)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversiti Tunku Abdul RahmanKamparMalaysia
  2. 2.School of Computer ScienceThe University of Nottingham Malaysia CampusSemenyihMalaysia
  3. 3.School of Computing and InformaticsUniversiti Teknologi BruneiBandar Seri BegawanBrunei
  4. 4.School of Computer SciencesUniversiti Sains MalaysiaGelugorMalaysia
  5. 5.Forensic ScienceprogramUniversiti Sains MalaysiaKubang KerianMalaysia

Personalised recommendations