An advanced scatter search design for skull-face overlay in craniofacial superimposition

Craniofacial superimposition is a forensic identification technique where photographs or video shots of a missing person are compared with a skull found in order to determine whether that is the same person. The second stage of this complex forensic process, named skull-face overlay, aims to achieve the best overlay of the skull 3D model and the 2D image of the face. In this paper, we aim to propose a new skull-face overlay method based on the scatter search evolutionary algorithm. This new design exploits problem-specific information in order to achieve faster and more robust solutions. The performance of our proposal is compared to the current best performing approach in the field of automatic skull-face overlay. Results on six real-world identification cases previously solved by the Physical anthropology lab at the University of Granada (Spain) are considered in our experimental study. The proposed method has shown a very accurate and robust performance when solving the latter six face-skull overlay problem instances.

[1]  Darren Robinson,et al.  A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential , 2009, Appl. Soft Comput..

[2]  Francisco Herrera,et al.  Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies , 2006, Eur. J. Oper. Res..

[3]  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.

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

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

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

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

[8]  Oscar Cordón,et al.  Feature-based image registration by means of the CHC evolutionary algorithm , 2006, Image Vis. Comput..

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

[10]  Debjani Chakraborty,et al.  A new approach to fuzzy distance measure and similarity measure between two generalized fuzzy numbers , 2010, Appl. Soft Comput..

[11]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[12]  Christian Roux,et al.  Genetic algorithms for a robust 3-D MR-CT registration , 2000, IEEE Transactions on Information Technology in Biomedicine.

[13]  Oscar Cordón,et al.  Scatter Search for the Point-Matching Problem in 3D Image Registration , 2008, INFORMS J. Comput..

[14]  Hitoshi Iba,et al.  Enhancing differential evolution performance with local search for high dimensional function optimization , 2005, GECCO '05.

[15]  Francisco Herrera,et al.  Real-Coded Memetic Algorithms with Crossover Hill-Climbing , 2004, Evolutionary Computation.

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

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

[18]  Kim L. Boyer,et al.  Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  F. Glover HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[20]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[21]  A K Ghosh,et al.  An economised craniofacial identification system. , 2001, Forensic science international.

[22]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

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

[24]  Aly A. Farag,et al.  A new genetic-based technique for matching 3-D curves and surfaces , 1999, Pattern Recognit..

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

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

[27]  Josef Tvrdík Adaptation in differential evolution: A numerical comparison , 2009, Appl. Soft Comput..

[28]  W A Aulsebrook,et al.  Superimposition and reconstruction in forensic facial identification: a survey. , 1995, Forensic science international.

[29]  Kim L. Boyer,et al.  Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure , 2004, Series in Machine Perception and Artificial Intelligence.

[30]  Ann H. Ross Use of Digital Imaging in the Identification of Fragmentary Human Skeletal Remains: A Case from the Republic of Panama , 2004 .

[31]  V. Leitáo,et al.  Computer Graphics: Principles and Practice , 1995 .

[32]  Marc Parizeau,et al.  Human-competitive lens system design with evolution strategies , 2008, Appl. Soft Comput..

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

[34]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[36]  J. Clement,et al.  Craniofacial identification by computer-mediated superimposition. , 2006, The Journal of forensic odonto-stomatology.

[37]  Y. Alakoç,et al.  The identification of a dismembered human body: a multidisciplinary approach. , 2003, Forensic science international.

[38]  M. Carter Computer graphics: Principles and practice , 1997 .

[39]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[40]  Ahmet Yardimci,et al.  Soft computing in medicine , 2009, Appl. Soft Comput..

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

[42]  Patrick A. Fitzhorn,et al.  A methodology for near-optimal computational superimposition of two-dimensional digital facial photographs and three-dimensional cranial surface meshes. , 1991, Journal of forensic sciences.

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

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

[45]  Rafael Martí,et al.  Scatter Search: Diseño Básico y Estrategias avanzadas , 2002, Inteligencia Artif..

[46]  L. J. Eshelman,et al.  chapter Real-Coded Genetic Algorithms and Interval-Schemata , 1993 .