Computer-based craniofacial superimposition in forensic identification using soft computing

  • B. Rosario Campomanes-Álvarez
  • Óscar Cordón
  • Sergio Damas
  • Óscar Ibáñez
Original Research

Abstract

One of the most important tasks in forensic anthropology is human identification. Over the past decades, forensic anthropologists have focused on improving techniques to increase the accuracy of identification. Following a thorough examination of unidentified human remains, the investigator chooses a specific identification technique to be applied, depending on the availability of ante mortem and post mortem data. Craniofacial superimposition is a forensic process in which photographs of a missing person are compared with a skull in order to determine whether is the individual depicted and the skeletal remains are the same person. After more than one century of development, craniofacial superimposition has become an interdisciplinary research field where computer science has acquired a key role as a complement of forensic sciences. Moreover, the availability of new digital equipment has resulted in a significant advance in the applicability of this forensic identification technique. In this paper, we review a semi-automatic method devised to assist the forensic anthropologist in the identification process using craniofacial superimposition. The technique is based on a three-stage methodology. The first two are performed automatically by soft computing techniques. However, the final decision corresponds to the forensic expert. The performance of the proposed method is illustrated using several real-world identification cases.

Keywords

Forensic identification Craniofacial superimposition Skull 3D model reconstruction Skull-face overlay Evolutionary algorithms Fuzzy landmarks 

Notes

Acknowledgments

This work has been supported by the Spanish Ministerio de Educación y Ciencia under the project TIN2009-07727. The authors would like to thank the team of the Physical Anthropology Laboratory of the University of Granada for providing us with real-world cases for our analysis.

References

  1. Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of Evolutionary Computation. IOP Publishing Ltd and Oxford University Press, BristolCrossRefMATHGoogle Scholar
  2. Bertillon A (1896) Signaletic instructions: including the theory and practice of anthropometrical identification. The Werner Company, ChicagoGoogle Scholar
  3. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans on Pattern Anal and Mach Intell 14:239–256CrossRefGoogle Scholar
  4. Beyer HG, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput 5:250–270CrossRefGoogle Scholar
  5. Bonissone P (1997) Soft computing: the convergence of emerging reasoning technologies. Soft Comput 1:6–18CrossRefGoogle Scholar
  6. Broca P (1875) Instructions craniologiques et craniométriques de la Société d’Anthropologie de Paris. In: Mason G (ed) Paris, pp 63–96Google Scholar
  7. Burns K (2007) Forensic anthropology training manual. Pearson/Prentice-Hall, UtahGoogle Scholar
  8. Chandra Sekharan P (1993) Positioning the skull for superimposition. In: Iscan MY, Helmer R (eds) Forensic analysis of the skull. Wiley-Liss, New York, pp 105–118Google Scholar
  9. Cordón O, Damas S, Santamaría J (2006) Feature-based image registration by means of the CHC evolutionary algorithm. Image Vis Comput 22:525–533CrossRefGoogle Scholar
  10. Dalley G, Flynn P (2001) Range image registration: a software platform and empirical evaluation. In: Third international conference on 3-D digital imaging and modeling (3DIM’01), pp 246–253Google Scholar
  11. Damas S, Cordón O, Santamaría J (2011a) Medical image registration using evolutionary computation: an experimental survey. IEEE Comput Intell Mag 6(4):26–42CrossRefGoogle Scholar
  12. Damas S, Cordón O, Ibáñez O, Santamaría J, Alemán I, Navarro F, Botella M (2011b) Forensic identification by computer-aided craniofacial superimposition: a survey. ACM Comput Surv 43(4):1–27Google Scholar
  13. De Angelis D, Sala R, Cantatore A, Grandi M, Cattaneo C (2009) A new computer-assisted technique to aid personal identification. Int J Leg Med 123(4):351–356CrossRefGoogle Scholar
  14. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9:115–148MathSciNetMATHGoogle Scholar
  15. Diamond P, Kloeden P (2000) Metric topology of fuzzy numbers and fuzzy analysis. In: Dubois D, Prade H (eds) Fundamentals of fuzzy sets. Kluwer, Boston, pp 583–637CrossRefGoogle Scholar
  16. Dru F, Wachowiak MP, Peters TM (2006) An ITK framework for deterministic global optimization for medical image registration. Proc SPIE Med Imaging Image Process 6144:1–12Google Scholar
  17. Eiben A, Smith J (2003) Introduction to evolutionary computing (natural computing series). Springer, BerlinGoogle Scholar
  18. Eshelman LJ (1991) The CHC adaptive search algorithm: how to safe search when engaging in nontraditional genetic recombination. In: Rawlins GJE (ed) Foundations of genetic algorithms 1. Morgan Kaufmann, San Mateo, pp 265–283Google Scholar
  19. Eshelman LJ (1993) Real-coded genetic algorithms and interval schemata. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo, pp 187–202Google Scholar
  20. Fenton TW, Heard AN, Sauer NJ (2008) Skull-photo superimposition and border deaths: identification through exclusion and the failure to exclude. J Foren Sci 53(1):34–40CrossRefGoogle Scholar
  21. George RM (1993) Anatomical and artistic guidelines for forensic facial reconstruction. In: Iscan MY, Helmer R (eds) Forensic analysis of the skull 16. Wiley-Liss, New York, pp 215–227Google Scholar
  22. Ghosh A, Sinha P (2001) An economised craniofacial identification system. J Foren Sci Int 117(1–2):109–119Google Scholar
  23. Glaister J, Brash J (1937) Medico-legal aspects of the Ruxton case. E & S Livingstone, EdinburghGoogle Scholar
  24. Glover F (1977) Heuristic for integer programming using surrogate constraints. J Decis Sci 8:156–166CrossRefGoogle Scholar
  25. González RC, Woods RE (2008) Digital image processing. Pearson Prentice, Upper Saddle RiverGoogle Scholar
  26. Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comp 9(2):159–195CrossRefGoogle Scholar
  27. Hearn D, Baker MP (1997) Computer graphics: C version. Prentice Hall, Upper Saddle RiverGoogle Scholar
  28. Ibáñez O, Ballerini L, Cordón O, Damas S, Santamaría J (2009a) An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification. Inf Sci 179(23):3998–4028CrossRefGoogle Scholar
  29. Ibáñez O, Cordón O, Damas S, Santamaría J (2009b) Multimodal genetic algorithms for craniofacial superimposition. In: Raymond C (ed) Nature-inspired informatics for intelligent applications and knowledge discovery: implications in business, science and engineering, pp 119–142Google Scholar
  30. Ibáñez O, Cordón O, Damas S, Santamaría J (2011) Modeling the skull-face overlay uncertainty using fuzzy sets. IEEE Trans Fuzzy Syst 19(5):946–959CrossRefGoogle Scholar
  31. Ibáñez O, Cordón O, Damas S, Santamaría J (2012a) An advanced scatter search design for skull-face overlay in craniofacial superimposition. Experts Syst Appl 39(1):1459–1473Google Scholar
  32. Ibáñez O, Cordón O, Damas S (2012b) A cooperative coevolutionary approach dealing with the skull-face overlay uncertainty in forensic identification by craniofacial superimposition. Soft Comput 16(5):797–808CrossRefGoogle Scholar
  33. Ikeuchi K, Sato Y, Nishino K, Sato I (2001) Modeling from reality: photometric aspect. Trans Virtual Real Soc Jpn 4(4):623–630Google Scholar
  34. Iscan M (1981) Integral forensic anthropology. Pract Anthropol 3(4):21–30Google Scholar
  35. Iscan MY (1993) Introduction to techniques for photographic comparison. In: Iscan MY, Helmer R (eds) Forensic analysis of the skull. Wiley, New York, pp 57–90Google Scholar
  36. Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flow shop scheduling. IEEE Trans Evol Comp 7(2):204–223CrossRefGoogle Scholar
  37. Jayaprakash PT, Srinivasan GJ, Amravaneswaran MG (2001) Craniofacial morphoanalysis: a new method for enhancing reliability while identifying skulls by photosuperimposition. Foren Sci Int 117:121–143CrossRefGoogle Scholar
  38. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156CrossRefGoogle Scholar
  39. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Trans Evol Comp 9(5):474–488CrossRefGoogle Scholar
  40. Krogman WM, Iscan MY (1986) The human skeleton in forensic medicine, 2nd edn. Charles C. Thomas, SpringfieldGoogle Scholar
  41. Laguna M, Martí R (2003) Scatter search: methodology and implementations. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  42. Martin R, Saller K (1966) Lehrbuch der Anthropologie in Systematischer Darstellung (in German) Gustav Fischer Verlag, StuttgartGoogle Scholar
  43. Nickerson BA, Fitzhorn PA, Koch SK, Charney M (1991) A methodology for near-optimal computational superimposition of two dimensional digital facial photographs and three-dimensional cranial surface meshes. J Foren Sci 36(2):480–500Google Scholar
  44. Park HK, Chung JW, Kho HS (2006) Use of hand-held laser scanning in the assessment of craniometry. J Foren Sci Int 160:200–206CrossRefGoogle Scholar
  45. Pesce Delfino V, Colonna M, Vacca E, Potente F, Introna F (1986) Computer-aided skull/face superimposition. Amer J Foren Med Pathol 7(3):201–212CrossRefGoogle Scholar
  46. Pétrowski A (1996) A clearing procedure as a niching method for genetic algorithms. In: Proceedings of IEEE international conference on evolutionary computation, pp 798–803Google Scholar
  47. Powell M (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7:155–162MathSciNetCrossRefMATHGoogle Scholar
  48. Santamaría J, Cordón O, Damas S, Alemán I, Botella M (2007a) Evolutionary approaches for automatic 3D modeling of skulls in forensic identification. Applications of evolutionary computing. In: Giacobini M et al (eds) Lecture notes in computer science, vol 4448. Springer, Berlin, pp 415–422Google Scholar
  49. Santamaría J, Cordón O, Damas S, Alemán I, Botella M (2007b) A scatter search-based technique for pair-wise 3D range image registration in forensic anthropology. Soft Comput 11(9):819–828CrossRefGoogle Scholar
  50. Santamaría J, Cordón O, Damas S, García-Torres JM, Quirin A (2009a) Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft Comput 13(8–9):883–904CrossRefGoogle Scholar
  51. Santamaría J, Cordón O, Damas S, Ibáñez O (2009b) Tackling the coplanarity problem in 3D camera calibration by means of fuzzy landmarks: a performance study in forensic craniofacial superimposition. IEEE Int Conf Comp Vis 926:1686–1693Google Scholar
  52. Santamaría J, Cordón O, Damas S (2010) A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling. Comp Vis Image Underst 115(9):1340–1354CrossRefGoogle Scholar
  53. Seta S, Yoshino M (1993) A combined apparatus for photographic and video superimposition. In: Iscan MY, Helmer R (eds) Forensic analysis of the skull. Wiley, New York, pp 161–169Google Scholar
  54. Silva L, Bellon O, Boyer K (2005) Robust range image registration using genetic algorithms and the surface interpenetration measure. In: Series in machine perception and artificial intelligence. World Scientific Publishing Co. Pte. Ltd., SingaporeGoogle Scholar
  55. Solis FJ, Wets RJB (1981) Minimization by random search techniques. In: Informs (ed) Mathematics of operations research, vol 6, USA, pp 19–30Google Scholar
  56. Stephan CN (2009) Craniofacial identification: techniques of facial approximation and craniofacial superimposition. In: Blau S, Ubelaker DH (eds) Handbook of forensic anthropology and archaeology. Left Coast Press, California, pp 304–321Google Scholar
  57. Stephan CN, Simpson E (2008a) Facial soft tissue depths in craniofacial identification—part I: an analytical review of the published adult data. J Foren Sci 53(6):1257–1272Google Scholar
  58. Stephan CN, Simpson E (2008b) Facial soft tissue depths in craniofacial identification—part I: an analytical review of the published sub-adult data. J Foren Sci 53(6):1273–1279Google Scholar
  59. Storn R (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetCrossRefMATHGoogle Scholar
  60. Taylor J, Brown K (1998) Superimposition techniques. In: Clement J, Ranson D (eds) Craniofacial identification in forensic medicine. Arnold, London, pp 151–164Google Scholar
  61. Ubelaker DH (2000) A history of Smithsonian-FBI collaboration in forensic anthropology, especially in regard to facial imagery. Foren Sci Commun 2(4)Google Scholar
  62. Ubelaker DH, Bubniak E, O’Donnell G (1992) Computer-assisted photographic superimposition. J Foren Sci 37(3):750–762Google Scholar
  63. Yamany SM, Ahmed MN, Farag AA (1999) A new genetic-based technique for matching 3D curves and surfaces. Pattern Recognit 32:1817–1820CrossRefGoogle Scholar
  64. Yao J, Goh KL (2006) A refined algorithm for multisensor image registration based on pixel migration. IEEE Trans Image Process 15(7):1839–1847CrossRefGoogle Scholar
  65. Yoshino M, Imaizumi K, Miyasaka S, Seta S (1995) Evaluation of anatomical consistency in craniofacial superimposition images. Foren Sci Int 74:125–134CrossRefGoogle Scholar
  66. Yoshizawa S, Belyaev A, Seidel HP (2005) Fast and robust detection of crest lines on meshes. In: SPM’05: proceedings of the 2005 ACM symposium on solid and physical modeling. ACM Press, New York, pp 227–232Google Scholar
  67. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353MathSciNetCrossRefMATHGoogle Scholar
  68. Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vis 13(2):119–152CrossRefGoogle Scholar
  69. Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • B. Rosario Campomanes-Álvarez
    • 1
  • Óscar Cordón
    • 2
    • 3
  • Sergio Damas
    • 1
  • Óscar Ibáñez
    • 1
  1. 1.European Centre for Soft ComputingMieresSpain
  2. 2.Department of Computer Science and Artifical IntelligenceUniversity of GranadaGranadaSpain
  3. 3.Research Center on Information and Communication Technologies (CITIC-UGR)University of GranadaGranadaSpain

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