Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data

  • Adam KortylewskiEmail author
  • Thomas Albrecht
  • Thomas Vetter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9008)


Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.


Image Retrieval Interest Point Periodic Pattern Translational Symmetry Crime Scene 
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.



This Project was supported by the Swiss Comission for Technology and Innovation (CTI) project 13932.1 PFES-ES. The authors thank the German State Criminal Police Offices of Niedersachsen and Bayern and the company forensity ag for their valuable support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Adam Kortylewski
    • 1
    Email author
  • Thomas Albrecht
    • 1
  • Thomas Vetter
    • 1
  1. 1.Departement of Mathematics and Computer ScienceUniversity of BaselBaselSwitzerland

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