Automatic Extraction and Classification of Footwear Patterns

  • Maria Pavlou
  • Nigel M. Allinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Identification of the footwear traces from crime scenes is an important yet largely forgotten aspect of forensic intelligence and evidence. We present initial results from a developing automatic footwear classification system. The underlying methodology is based on large numbers of localized features located using MSER feature detectors. These features are transformed into robust SIFT or GLOH descriptors with the ranked correspondence between footwear patterns obtained through the use of constrained spectral correspondence methods. For a reference dataset of 368 different footwear patterns, we obtain a first rank performance of 85% for full impressions and 84% for partial impressions.


Scale Invariant Feature Transform Crime Scene Automatic Extraction Maximally Stable Extremal Region Correct Recognition Rate 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maria Pavlou
    • 1
  • Nigel M. Allinson
    • 1
  1. 1.University of SheffieldSheffieldUK

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