Abstract
Craniofacial superimposition is a forensic process where photographs or video shots of a missing person are compared with the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned three-dimensional skull model against the face photo/video shot), the forensic anthropologist can try to establish whether that is the same person. The whole process is influenced by inherent uncertainty mainly because two objects of different nature (a skull and a face) are involved. In previous work, we categorized the different sources of uncertainty and introduced the use of imprecise landmarks to tackle most of them. In this paper, we propose a novel approach, a cooperative coevolutionary algorithm, to deal with the use of imprecise cephalometric landmarks in the skull–face overlay process, the main task in craniofacial superimposition. Following this approach we are able to look for both the best projection parameters and the best landmark locations at the same time. Coevolutionary skull–face overlay results are compared with our previous fuzzy-evolutionary automatic method. Six skull–face overlay problem instances corresponding to three real-world cases solved by the Physical Anthropology Lab at the University of Granada (Spain) are considered. Promising results have been achieved, dramatically reducing the run time while improving the accuracy and robustness.
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Notice that, mean square error is not used because of its negative effect when image ranges are normalized in [0,1].
Despite Fuzzy ME is not a fuzzy number but a number, we use the same notation proposed in Ibáñez et al. (2011) to avoid misunderstanding.
References
Bloch I (1999) On fuzzy distances and their use in image processing under imprecision. Pattern Recognit 32:1873–1895
Bucci A, Pollack JB (2005) On identifying global optima in cooperative coevolution. In: Genetic and evolutionary computation conference (GECCO’05), ACM, pp 539–544
Buckley JJ, Eslami E (1997) Fuzzy plane geometry I: points and lines. Fuzzy Sets Syst 86(2):179–187
Damas S, Cordón O, Ibáñez O, Santamaría J, Alemán I, Navarro F, Botella M (2011) Forensic identification by computer-aided craniofacial superimposition: a survey. ACM Comput Surv 43(4). http://doi.acm.org/1978802.1978806
Deb K, Beyer H (1999) Self-adaptation in real-parameter genetic algorithms with simulated binary crossover. In: Proceedings of the genetic and evolutionary computation conference
Diamond P, Kloeden P (2000) Metric topology of fuzzy numbers and fuzzy analysis. In: Dubois D, Prade H (eds) Fundamentals of fuzzy sets. The handbooks of fuzzy sets, chap 11, Kluwer Academic, pp 583–637
Dubois D, Prade H (1983) On distance between fuzzy points and their use for pausible reasoning. In: International conference on systems, man and cybernetics, pp 300–303
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer
Fenton TW, Heard AN, Sauer NJ (2008) Skull–photo superimposition and border deaths: identification through exclusion and the failure to exclude. J Forensic Sci 53(1):34–40
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195
Hearn D, Baker MP (1997) Computer graphics (2nd edn.) C version. Prentice-Hall, Upper Saddle River
Hee-Kyung P, Jin-Woo C, Hong-Seop K (2006) Use of hand-held laser scanning in the assessment of craniometry. Forensic Sci Int 160:200–206
Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for the behavioural analysis. Artif Intell Rev 12(4):265–319
Ibáñez O, Ballerini L, Cordón O, Damas S, Santamaría J (2009) An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification. Inf Sci 179:3998–4028
Ibáñez O, Cordón O, Damas S, Santamaría J (2011) Modeling the skull–face overlay uncertainty using fuzzy sets. IEEE Trans Fuzzy Syst 19(5):946–959
Iscan MY (1993) Introduction to techniques for photographic comparison. In: Iscan MY, Helmer R (eds) Forensic analysis of the skull, Wiley, pp 57–90
Krogman WM, Iscan MY (1986) The human skeleton in forensic medicine, 2nd edn. Charles C. Thomas, Springfield
Paredis J (1995) Coevolutionary computation. Artif Life 2:355–375
Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29
Richtsmeier J, Paik C, Elfert P, Cole TM, Dahlman F (1995) Precision, repeatability and validation of the localization of cranial landmarks using computed tomography scans. Cleft Palate-Craniofacial J 32(3):217–227
Rosin CD, Belew RK (1997) New methods for competitive coevolution. Evol Comput 5(1):1–29
Santamaría J, Cordón O, Damas S, Alemán I, Botella M (2007) A scatter search-based technique for pair-wise 3D range image registration in forensic anthropology. Soft Comput 11(9):819–828
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):883–904
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. In: IEEE international conference on computer vision, Kyoto, Japan, pp 1686–1693
Santamaría J, Cordón O, Damas S (2010) A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling. Comput Vis Image Underst 115(9):1340–1354
Stephan CN, Simpson EK (2008a) Facial soft tissue depths in craniofacial identification (part I): an analytical review of the published adult data. J Forensic Sci 53(6):1257–1272
Stephan CN, Simpson EK (2008b) Facial soft tissue depths in craniofacial identification (part II): an analytical review of the published sub-adult data. J Forensic Sci 53(6):1273–1279
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 304–321
Ubelaker DH (2000) A history of Smithsonian–FBI collaboration in forensic anthropology, especially in regard to facial imagery. Forensic Sc Commun 2(4):(online)
Wiegand RP (2003) An analysis of cooperative coevolutionary algorithms. PhD thesis, George Mason University, Fairfax, Virginia
Wiegand RP, Liles W, De Jong K (2001) An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference, pp 1235–1242
Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000
Acknowledgments
This work is supported by the Spanish Ministerio de Educación y Ciencia (ref. TIN2009–07727), including EDRF fundings. We would like to acknowledge all the team of the Physical Anthropology Lab at the University of Granada (headed by Dr. Botella and Dr. Alemán) for their support during the data acquisition and validation processes. Part of the experiments related to this work was supported by the computing resources at the Supercomputing Center of Galicia (CESGA), Spain.
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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, 797–808 (2012). https://doi.org/10.1007/s00500-011-0770-8
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DOI: https://doi.org/10.1007/s00500-011-0770-8