Modeling the Skull–Face Overlay Uncertainty Using Fuzzy Sets

Craniofacial superimposition (CS) 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 3-D skull model against the face photo/video shot), the forensic anthropologist can try to establish whether it 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 this paper, we extend our previous evolutionary-algorithm-based method to automatically superimpose the 3-D skull model and the 2-D face photo with the aim to overcome the limitations that are associated with the different sources of uncertainty, which are present in the problem. Two different approaches to handle the imprecision will be proposed: weighted and fuzzy-set-theory-based landmarks. The performance of the new proposal is analyzed, considering five skull-face overlay problem instances that correspond to three real-world cases solved by the Physical Anthropology Laboratory, University of Granada, Granada, Spain. The experimental study that is developed shows how the fuzzy-set-based approach clearly outperforms the previous crisp solution. Finally, the proposed method is validated by the comparison of its outcomes with respect to those manually achieved by the forensic experts in nine skull-face overlay problem instances.

[1]  Oscar Cordón,et al.  Tackling the coplanarity problem in 3D camera calibration by means of fuzzy landmarks: a performance study in forensic craniofacial superimposition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[2]  J. J. Buckley,et al.  Fuzzy plane geometry I: Points and lines , 1997, Fuzzy Sets Syst..

[3]  D. Ubelaker A History of Smithsonian-FBI Collaboration in Forensic Anthropology, Especially in Regard to Facial Imagery , 2000 .

[4]  Isabelle Bloch,et al.  On fuzzy distances and their use in image processing under imprecision , 1999, Pattern Recognit..

[5]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[6]  Oscar Cordón,et al.  Performance evaluation of memetic approaches in 3D reconstruction of forensic objects , 2008, Soft Comput..

[7]  George J. Klir,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.

[8]  Oscar Cordón,et al.  Forensic identification by computer-aided craniofacial superimposition: A survey , 2011, CSUR.

[9]  W. Marsden I and J , 2012 .

[10]  Thomas S. Huang,et al.  3D Face Processing: Modeling, Analysis and Synthesis , 2004 .

[11]  Oscar Cordón,et al.  An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification , 2009, Inf. Sci..

[12]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[13]  P. Kloeden,et al.  Metric Topology of Fuzzy Numbers and Fuzzy Analysis , 2000 .

[14]  W. M. Krogman The human skeleton in forensic medicine. I. , 1963, Postgraduate medicine.

[15]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[16]  M. Pauline Baker,et al.  Computer Graphics, C Version , 1996 .

[17]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[18]  Chen Yi-song,et al.  Precision �� , 2022 .

[19]  Carl N Stephan,et al.  Facial Soft Tissue Depths in Craniofacial Identification (Part I): An Analytical Review of the Published Adult Data * , 2008, Journal of forensic sciences.

[20]  Oscar Cordón,et al.  A scatter search-based technique for pair-wise 3D range image registration in forensic anthropology , 2006, Soft Comput..

[21]  P. Sinha A symmetry perceiving adaptive neural network and facial image recognition. , 1998, Forensic science international.

[22]  Susan I. Schultz,et al.  Introduction to Techniques , 1993 .

[23]  Oscar Cordón,et al.  A New Approach to Fuzzy Location of Cephalometric Landmarks in Craniofacial Superimposition , 2009, IFSA/EUSFLAT Conf..

[24]  Joan T. Richtsmeier,et al.  Precision, Repeatability, and Validation of the Localization of Cranial Landmarks Using Computed Tomography Scans , 1995, The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association.

[25]  Zicheng Liu,et al.  Model-based bundle adjustment with application to face modeling , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[26]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[27]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[28]  T. Fenton,et al.  Skull‐Photo Superimposition and Border Deaths: Identification Through Exclusion and the Failure to Exclude * , 2008, Journal of forensic sciences.

[29]  Rama Chellappa,et al.  Face Processing: Advanced Modeling and Methods , 2006, J. Electronic Imaging.

[30]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[31]  Karen Ramey Burns,et al.  Forensic Anthropology Training Manual , 2006 .

[32]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[33]  C. Stephan,et al.  Facial Soft Tissue Depths in Craniofacial Identification (Part II): An Analytical Review of the Published Sub‐Adult Data * , 2008, Journal of forensic sciences.