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10 th European Meeting for SP/TM - ENFSI Marks WG Bled , 7 th June 2013

AUTOMATED PATTERN RECOGNITION SYSTEM FOR SHOE TRACKS. 10 th European Meeting for SP/TM - ENFSI Marks WG Bled , 7 th June 2013 . Origin of the P roject. Titel : NEXT GENERATION OF AUTOMATIC PATTERN RECOGNITION SYSTEMS FOR FORENSIC SHOE TRACK APPLICATIONS

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10 th European Meeting for SP/TM - ENFSI Marks WG Bled , 7 th June 2013

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  1. AUTOMATED PATTERN RECOGNITION SYSTEM FOR SHOE TRACKS 10thEuropean Meeting for SP/TM - ENFSI Marks WG Bled, 7th June 2013

  2. Origin oftheProject Titel: NEXT GENERATION OF AUTOMATIC PATTERN RECOGNITION SYSTEMS FOR FORENSIC SHOE TRACK APPLICATIONS Core Target: Development of an automated footwear retrieval system Participation: forensity(industry partner) - Knows the challenges and needs of shoe track specialists - Responsible for a praxis oriented research (e.g. testing material) - Makes from the research results an application University of Basel, Computer Vision Group (research institute) - Has profound technological know-how in visual computing - Does the research - Delivers a practical procedure in image matching Financning: Commission of Technology and Innovation of the Swiss Government

  3. OverviewPrevious Research Synthetic Data 2005 De Chazal DFT 81% 2005 Zhang Edge Histogram88% 2006 PavlouSIFT 85% 2006 Ghouti Wavelet Decomposition100% 2007 Crookes Phase OnlyCorrelation82% 2008 GuehamCorrelation Filter 94% 2008 Patil Gabor Feature Maps91% 2008 AlGarni Hu Moments 99% 2008 Pavlou SIFT codebook92% 2009 Nibouche SIFT + RANSAC97% 2013 Wei SIFT + Correlation 96%

  4. OverviewPrevious Research Synthetic Data VS Real Case Data 2005 De Chazal DFT 81% 2005 Zhang Edge Histogram88% 2006 Pavlou SIFT 85% 2006 Ghouti Wavelet Decomposition100% 2007 Crookes Phase OnlyCorrelation82% 2008 GuehamCorrelation Filter 94% 2008 Patil Gabor Feature Maps91% 2008 AlGarni Hu Moments 99% 2008 Pavlou SIFT codebook92% 2009 Nibouche SIFT + RANSAC 97% 2013 Wei SIFT + Correlation 96%

  5. OverviewPrevious Research Real Case Data • 2009 DardiMahalanobisMap50% • CervelliSpectral Analysis 17% • database not publiclyavailable • notolerancetorotation, scale, translation

  6. OverviewPrevious Research Real Case Data • 2009 DardiMahalanobisMap50% • CervelliSpectral Analysis 17% • database not publiclyavailable • notolerancetorotation, scale, translation • 2011 Tang Graph Embedding 70% • databasenot publiclyavailable • toostrong assumptions( lines, ellipses, circles ) • 2009 DardiMahalanobisMap50% • CervelliSpectral Analysis 17% • database not publiclyavailable • notolerancetorotation, scale, translation

  7. ShoeprintMatching System Overview Input Data: Database: User Interaction: • Rotation • Cropping • ScaleNormalization • Mark Outsole • Mark Regionsof Interest Classification: - Feature ExtractionandMatching

  8. Input Data - Issues Input Data User Interaction DB Classification AdaptedfromCervelli et al. 2009

  9. Input Data - Source • Over 20’000 crimesceneshoeprintsandreferenceprintsprovidedby: • Test-Database currentlycontains • 220 shoeprints& 1115 referenceprints • Still underdevelopment 9

  10. User Interaction I/II Input Data User Interaction DB Classification

  11. User Interaction II/II Input Data User Interaction DB Classification

  12. Realignment Input Data scale rotation User Interaction Realignment DB Classification

  13. ClassificationResults Cumulative Match Score 220 Shoeprints / 1115 Reference Prints Input Data User Interaction Realignment DB Classification

  14. Conclusion • Overall promising results in theresearchfield • Still a varietyofchallanges • Unconstrained Noise, Photos, Transformation invariance • Need forstandarddatasets& researchprojects

  15. Questions adam.kortylewski@unibas.ch

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