A Method for Footprint Range Image Segmentation and Description

  • Yihong Ding
  • Xijian Ping
  • Min Hu
  • Tao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper, we firstly present a novel footprint range image segmentation method using the principal curvatures and the principal directions. Utilizing the principal curvatures information, we detect the peak areas as the seeds, and apply region growing to locate the edges of each patch. We apply the edge detection technology to the region growth rules, so the boundary localization is precise. To obtain more stable edge information, a multi-scale fusion approach is proposed to integrate the segmentation results calculated at different fitting sizes. After the segmentation, according to the shape characteristics of footprint, we use superquadric and saddle models to describe shape features of each patch. The experiments results on footprint range images show that the segmented patches and the descriptions represent footprint biometric information effectively and set a reliable basis for the further recognition.


Segmentation Result Principal Curvature Principal Direction Range Image Plaster Cast 
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.
    Chirillo, J., Blaul, S.: Implementing Biometric Security. John Wiley & Sons, Chichester (2003)Google Scholar
  2. 2.
    Wang, Q.J., Han, J.L., Zheng, D.C., Wu, Z.L.: The Quantitative Inspection of Footprint and Gait. Chinese People’s Public Security University Press (1992) (in chinese)Google Scholar
  3. 3.
    Tian, Y., Ping, X.J., Wang, Y.J.: A New Method about 3D Surface Recognition. In: Proceedings of the 5th Joint Conference on Information Sciences, vol. 1, pp. A147–A150 (2000)Google Scholar
  4. 4.
    Nakajima, K., Mizukami, Y., Tanaka, K., Tamura, T.: Footprint-Based Personal Recognition. IEEE Trans. on Biomedical Engineering 47, 1534–1537 (2000)CrossRefGoogle Scholar
  5. 5.
    Kennedy, R.B., Pressman, S., Chen, S., Petersen, P.H., Pressman, A.E.: Statistical Analysis of Barefoot Impressions. Journal of Forensic Sciences 48, 55–63 (2003)Google Scholar
  6. 6.
    Fan, T., Medioni, G., Nevatia, R.: Segmented description of 3-D surfaces. IEEE Journal of Robotics and Automation 3, 527–538 (1987)CrossRefGoogle Scholar
  7. 7.
    Bellon, O.R.P., Silva, L.: New Improvements to Range Image Segmentation by Edge Detection. IEEE Signal Processing Letters 9, 43–45 (2002)CrossRefGoogle Scholar
  8. 8.
    Jiang, X.Y., Bunke, H.: Edge Detection in Range Images Based on Scan Line Approximation. Computer Vision and Image Understanding 73, 183–199 (1999)CrossRefGoogle Scholar
  9. 9.
    Zhu, S.C., Yuille, A.: Region Competition: Unifying Snakes, Region Growing, and Bayes/ MDL for Multi-band Image Segmentation. IEEE Trans on PAMI 18, 884–900 (1996)Google Scholar
  10. 10.
    Besl, P.J., Jain, R.C.: Segmentation Through Variable Order Surface Fitting. IEEE Trans. on PAMI 10, 167–192 (1988)Google Scholar
  11. 11.
    Koster, K., Spann, M.: MIR: An Approach to Robust Clustering Application to Range Image Segmentation. IEEE Trans. on PAMI 22, 430–444 (2000)Google Scholar
  12. 12.
    Yokoya, N., Levine, D.: Range Image Segmentation Based on Differential Geometry: A Hybrid Approach. IEEE Trans. on PAMI 11, 634–649 (1989)Google Scholar
  13. 13.
    Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Trans. on PAMI 13, 583–598 (1991)Google Scholar
  14. 14.
    Vincent, L.: Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Trans. on Image Processing 2, 176–201 (1993)CrossRefGoogle Scholar
  15. 15.
    Leonardis, A., Jaklic, A., Solina, F.: Superquadrics for Segmenting and Modeling Range Data. IEEE Trans. on PAMI 19, 1289–1295 (1997)Google Scholar
  16. 16.
    Whaite, P., Ferrie, F.P.: From Uncertainty to Visual Exploration. IEEE Trans. on PAMI 13, 1038–1049 (1991)Google Scholar
  17. 17.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, London. Cambridge University Press, Cambridge (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yihong Ding
    • 1
  • Xijian Ping
    • 1
  • Min Hu
    • 1
  • Tao Zhang
    • 1
  1. 1.Zhengzhou Information Science and Technology InstituteZhengzhouChina

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