Evolutionary computing enriched ridge regression model for craniofacial reconstruction

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Abstract

Craniofacial reconstruction is one of the dominating research domains having vital significance towards forensic purposes as well as archaeological investigation needs. With a goal to enable an error-resilient and swift craniofacial reconstruction model, in this paper an evolutionary computing assisted enhanced regression model has been proposed. We have developed an enhanced over-fitting resilient regression model called Ridge Regression (RR) as a statistical method to perform craniofacial reconstruction using landmark points and skull-face/(tissue) skin features. Our proposed model incorporates a hybrid evolutionary computing scheme containing Particle Swarm Optimization (PSO) and Differential Evolution (DE) to perform feature point selection and landmark count reduction. Here, the prime objective is to reduce the feature sets and landmark that can eventually make craniofacial reduction process more time efficient and accurate. The performance assessment reveals that the proposed PSO-DEFS based RRM model outperforms existing approaches such as the least square support vector regression (LSSVR) and partial least square regression (PLSR).

Keywords

Craniofacial reconstrction Statistical method Evolutionary computing, ridge regression Particle swarm optimization, differential evolution 
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References

  1. 1.
    Berar M, Desvignes M, Bailly G, Panyan Y (2005) 3d statistical facial reconstruction. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. pp 365–370Google Scholar
  2. 2.
    Berar M, Tilotta FM, Glaunes JA, Rozenholc Y (2011) Craniofacial reconstruction as a prediction problem using a latent root regression model. Forensic Sci Int 210(1–3):228–236CrossRefGoogle Scholar
  3. 3.
    Claes P (2007) A robust statistical surface registration framework using implicit function representations: application in craniofacial reconstruction. In Faculteitingenieurswetenschappen, department Elektrotechniek, afdeling PSI, K.U. Leuven, LeuvenGoogle Scholar
  4. 4.
    Claes P, Vandermeulen D, De Greef S, Willems G, Suetens P (2006) Statistically deformable face models for cranio-facial reconstruction. J Comput Inf Technol 14(1):21–30CrossRefGoogle Scholar
  5. 5.
    Claes P, Vandermeulen D, De Greef S, Willems G, Suetens P (2006) Craniofacial reconstruction using a combined statistical model of face shape and soft tissue-depths: methodology and validation. Forensic Sci Int 159(1):147–158CrossRefGoogle Scholar
  6. 6.
    Claes P, Vandermeulen D, De Greef S, Willems G, Clement JG et al (2010) Computerized craniofacial reconstruction: conceptual framework and review. Forensic Sci Int 201(1):138–145CrossRefGoogle Scholar
  7. 7.
    Claes P, Vandermeulen D, De Greef S, Willems G, Clement JG, Suetens P (2010) Bayesian estimation of optimal craniofacial reconstructions. Forensic Sci Int 201:146–152CrossRefGoogle Scholar
  8. 8.
    De Greef S, Claes P, Mollemans W, Loubele M, Vandermeulen D, Suetens P, Willems G (2005) Semi-automated ultrasound facial soft tissue depth registration: method and validation. J Forensic Sci 50(6):JFS2004547–7Google Scholar
  9. 9.
    Deng Q, Zhou M, Shui W, Wu Z, Ji Y, Bai R (2011) A novel skull registration based on global and local deformations for craniofacial reconstruction. Forensic Sci Int 208:95–102CrossRefGoogle Scholar
  10. 10.
    Duan S, Yang D, Huang Y, Hu ZW (2014) Craniofacial reconstruction based on multilinear subspace analysis. Multimedia Tools Appl 73(2):809–823CrossRefGoogle Scholar
  11. 11.
    Hoerl A, Kennard R (1970) Ridge regression: biased estimation for nonorthogonal problems. Am Stat Assoc 12(1):55–67MATHGoogle Scholar
  12. 12.
    Hu Y, Zhou M, Wu Z (2009) An automatic dense point registration method for 3d face animation. In: Image and Signal Processing, 2009. CISP'09. 2nd International Congress on IEEE. 2009, pp 1–6Google Scholar
  13. 13.
    Huang D, Duan F, Deng Q, Wu Z, Zhou M (2011) Face reconstruction from skull based on partial least squares regression. In: Computational Intelligence and Security (CIS), 2011 SeventhInternational Conference on IEEE. 2011, pp 1118–1121Google Scholar
  14. 14.
    Li Y, Chang L, Qiao X, Liu R, Duan F (2014) Craniofacial reconstruction based on least square support vector regression. SMC pp 1147–1151Google Scholar
  15. 15.
    Lorensen WE, Harvey EC (1987) Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput Graph 21(4):163–169CrossRefGoogle Scholar
  16. 16.
    Paysan P, Luthi M, Albrecht T, Lerch A, Amberg B, et al. (2009) Face reconstruction from skull shapes and physical attributes. Pattern Recognition. Springer Berlin Heidelberg, pp. 232–241Google Scholar
  17. 17.
    Roozbeh M (2016) Robust ridge estimator in restricted semiparametric regression models. J Multivar Anal 147:127–144MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Tilotta F, Richard F, Glaunes JA, Berar M, Gey S, Verdeille S, Rozenholc Y, Gaudy JF (2009) Construction and analysis of a head CT-scan database for craniofacial reconstruction. Forensic Sci Int 191(1):112–e1Google Scholar
  19. 19.
    Turner WD, Brown RE, Kelliher TP, Tu PH, Taister MA, KWP M (2005) A novel method of automated skull registration for forensic facial approximation. Forensic Sci Int 154:149–158CrossRefGoogle Scholar
  20. 20.
    Vigneau E, Qannari M (2002) A new algorithm for latent root regression analysis. Comput Stat Data Anal 41:231–242MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Wilkinson C (2004) Forensic facial reconstruction. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of Science, New ValleyAssiut UniversityAssiutEgypt

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