A Privacy Algorithm for 3D Human Body Scans

  • Joseph Laws
  • Yang Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


In this paper, we explore a privacy algorithm that detects human private parts in a 3D scan dataset. The intrinsic human proportions are applied to reduce the search space by an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression. The feature is then detected using the relative measurements of the height and area factors. The method is tested on 100 datasets from CAESER database.


Radial Basis Function Template Match Curvature Feature Body Feature Privacy Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Suikerbuik, R., Tangelder, H., Daanen, H., Oudenhuijzen, A.: Automatic feature detection in 3D human body scans. In: Proceedings of SAE Digital Human Modeling Conference (2004) 04-DHM-52Google Scholar
  2. 2.
    Suikerbuik, C.A.M.: Automatic Feature Detection in 3D Human Body Scans. Master thesis INF/SCR-02-23, Institute of Information and Computer Sciences. Utrecht University (2002)Google Scholar
  3. 3.
    Forsyth, D.A., Fleck, M.M.: Automatic detection of human nudes. International Journal of Computer Vision 32(1), 63–77 (1999)CrossRefGoogle Scholar
  4. 4.
    Ioffe, S., Forsyth, D.A.: Probabilistic methods for finding people. International Journal of Computer Vision 43(1), 45–68 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Forsyth, D.A., Fleck, M.M.: Body Plans. In: Proc. CVPR 1997, pp. 678–683 (1997)Google Scholar
  6. 6.
    Forsyth, D.A., Fleck, M.M.: Identifying nude pictures. In: Proceeding of Third IEEE Workshop on Applications of Computer Vision, pp. 103–108 (1996)Google Scholar
  7. 7.
    Fleck, M.M., Forsyth, D.A., Bregler, C.: Finding naked people. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 593–602. Springer, Heidelberg (1996)Google Scholar
  8. 8.
  9. 9.
    Jones, P.R.M., Rioux, M.: Three-dimensional surface anthropometry: applications to the human body. Optics and Lasers in Engineering 28, 89–117 (1997)CrossRefGoogle Scholar
  10. 10.
    Robinette, K.M., Blackwell, S., Daanen, H.A.M., Fleming, S., Boehmer, M., Brill, T., Hoeferlin, D., Burnsides, D.: Civilian American and European Surface (2002)Google Scholar
  11. 11.
    Anthropometry Resource (CAESAR), Final Report, vol. I: Summary, AFRL-HE-WP-TR-2002-0169, United States Air Force Research Laboratory, Human Effectiveness Directorate, Crew System Interface Division, 2255 H Street, Wright-Patterson AFB OH 45433-7022 and SAE International, 400 Commonwealth Dr., Warrendale, PA 15096Google Scholar
  12. 12.
    Gordon, G.: Face recognition based on depth and curvature features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Champaign Illinois), pp. 108–110 (1992)Google Scholar
  13. 13.
    Calladine, C.R.: Gaussian curvature and shell structures. The Mathematics of Surfaces, pp. 179–196. Oxford University Press, Oxford (1985)Google Scholar
  14. 14.
    Li, P., Corner, B.D., Paquette, S.: Evaluation of a surface curvature based landmark extraction method for three dimensional head scans. In: International Ergonomics Conference, Seoul (2003)Google Scholar
  15. 15.
    Ratner, P.: 3-D human modeling and animation. John Wiley & Sons, Inc., Chichester (2003)Google Scholar
  16. 16.
    Liu, X.W.K., Drerup, B.: 3D Characterization and Localization of Anatomical Landmarks of the Foot. In: Proceeding (417), Biomedical Engineering. Acta Press (2004),
  17. 17.
    Bansal, M.: Analysis of curvature in genomic DNA,
  18. 18.
    Coleman, R., Burr, M., Souvaine, D., Cheng, A.: An intuitive approach to measuring protein surface curvature. Proteins: structure, function and bioinformatics 61(4), 1068–1074Google Scholar
  19. 19.
    Goldgof, D.B., Huang, T.S., Lee, H.: A Curvature-Based Approach to Terrain Recognition, November 1989, vol. 11(11), pp. 1213–1217 (1989)Google Scholar
  20. 20.
    Besl, P.J., Jain, R.C.: Three-dimensional object recognition. ACM Comput. Surveys 17(1), 75–145 (1985)CrossRefGoogle Scholar
  21. 21.
    Brady, M., Ponce, J., Yuille, A., Asada, H.: Describing surfaces. Comput. Vision, Graphics, Image Processing 32, 1–28 (1985)CrossRefGoogle Scholar
  22. 22.
    Fan, T.G., Medioni, G., Nevatia, R.: Description of surfaces from range data using curvature properties. In: Proc. CVPR (May 1986)Google Scholar
  23. 23.
    Chen, H.H., Huang, T.S.: Maximal matching of two three-dimensional point sets. In: Proc. ICPR (October 1986)Google Scholar
  24. 24.
    Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Trans. Pattern Anal. Machine Intell. PAMI-9(4), 532–550 (1987)CrossRefGoogle Scholar
  25. 25.
    Goldgof, D.B., Huang, T.S., Lee, H.: Feature extraction and terrain matching. In: Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, Ann Arbor, MI (May 1988)Google Scholar
  26. 26.
    Goldgof, D.B., Huang, T.S., Lee, H.: Curvature based approach to terrain recognition. Coord. Sci. Lab., Univ. Illinois, Urbana-Champaign, Tech. Note ISP-910 (April 1989)Google Scholar
  27. 27.
    Sonka, M., et al.: Image processing, analysis and machine vision. PWS Publishing (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Joseph Laws
    • 1
  • Yang Cai
    • 1
  1. 1.Visual Intelligence Studio, Cylab, CIC 2218Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations