A Texture Based Shoe Retrieval System for Shoe Marks of Real Crime Scenes

  • Francesca Dardi
  • Federico Cervelli
  • Sergio Carrato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Shoeprints found on the crime scene contain useful information for the investigator: being able to identify the make and model of the shoe that left the mark on the crime scene is important for the culprit identification. Semi-automatic and automatic systems have already been proposed in the literature to face the problem, however all previous works have dealt with synthetic cases, i.e. shoe marks which have not been taken from a real crime scene but are artificially generated with different noise adding techniques.

Here we propose a descriptor based on the Mahalanobis distance for the retrieval of shoeprint images. The performance test of the proposed descriptor is performed on real crime scenes shoe marks and the results are promising.


Scale Invariant Feature Transform Crime Scene Texture Region Canny Edge Detector Image Retrieval System 
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.
    James, S.H., Nordby, J.J.: Forensic Science: an introduction to scientific and investigative techniques, 2nd edn. CRC Press, Boca Raton (2005)Google Scholar
  2. 2.
    Girod, A.: Shoeprints: coherent exploitation and management. In: European Meeting for Shoeprint Toolmark Examiners, The Netherlands (1997)Google Scholar
  3. 3.
    Bodziak, W.J.: Footwear impression evidence: detection, recovery and examination, 2nd edn. CRC Press, Boca Raton (1999)Google Scholar
  4. 4.
    ENFSI WGM Conclusion Scale Committee: Conclusion scale for shoeprint and toolmarks examiners. J. Forensic Ident. 56, 255–265 (2006)Google Scholar
  5. 5.
    Girod, A.: Computerized classification of the shoeprints of burglars’ shoes. Forensic Sci. Int. 82, 59–65 (1996)CrossRefGoogle Scholar
  6. 6.
    Sawyer, N.: SHOE-FIT a computerized shoe print database. In: Proc. Eur. Convention Secur. Detect., pp. 86–89 (1995)Google Scholar
  7. 7.
    Ashley, W.: What shoe was that? The use of computerized image database to assist in identification. Forensic Sci. Int. 82, 7–20 (1996)CrossRefGoogle Scholar
  8. 8.
    Geradts, Z., Keijzer, J.: The image-database REBEZO for shoeprint with developments for automatic classification of shoe outsole designs. Forensic Sci. Int. 82, 21–31 (1996)CrossRefGoogle Scholar
  9. 9.
    Bouridane, A., Alexander, A., Nibouche, M., Crookes, D.: Application of fractals to the detection and classification of shoeprints. In: Proc. Int. Conf. Image Processing, vol. 1, pp. 474–477 (2000)Google Scholar
  10. 10.
    De Chazal, P., Flynn, J., Reilly, R.B.: Automated processing of shoeprint image based on the fourier transform for use in forensic science. IEEE Trans. Pattern Analysis Machine Intelligence 27, 341–350 (2005)CrossRefGoogle Scholar
  11. 11.
    Gueham, M., Bouridane, A., Crookes, D.: Automatic recognition of partial shoeprints based on phase-only correlation. In: Proc. Int. Conf. Image Processing, vol. 4, pp. 441–444 (2007)Google Scholar
  12. 12.
    Algarni, G., Amiane, M.: A novel technique for automatic shoeprint image retrieval. Forensic Sci. Int. 181, 10–14 (2008)CrossRefGoogle Scholar
  13. 13.
    Pavlou, M., Allinson, N.M.: Automated encoding of footwear patterns for fast indexing. Image Vision Computing 27, 402–409 (2009)CrossRefGoogle Scholar
  14. 14.
    Utsumi, A., Tetsutani, N.: Human detection using geometrical pixel value structures. In: Proc. 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 39–44 (2002)Google Scholar
  15. 15.
    Gonzalez, R.C., Woods, R.E.: Digital image processing, 3rd edn. Pearson Prentice Hall, Upper Saddle River (2008)Google Scholar
  16. 16.
  17. 17.
    Russ, J.C.: The image processing handbook, 2nd edn. CRC Press, Boca Raton (2005)zbMATHGoogle Scholar
  18. 18.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Patt. Anal. Mac. Intell. 22, 1090–1104 (2000)CrossRefGoogle Scholar
  19. 19.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. Int. J. Computer Vision 65, 43–72 (2005)CrossRefGoogle Scholar
  20. 20.
    Cervelli, F., Dardi, F., Carrato, S.: Towards an automatic footwear retrieval system for crime scene shoe marks: Comparison of different methods on synthetic and real shoe marks. To be published in 6th Int. Symp. on Image and Signal Processing and Analysis (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesca Dardi
    • 1
  • Federico Cervelli
    • 1
  • Sergio Carrato
    • 1
  1. 1.Dept. Electrical, Electronic and Information Engineering (DEEI)University of TriesteTriesteItaly

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