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

2. Origin of the Project
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. 2006 Ghouti Wavelet Decomposition 100%
Overview Previous Research
Synthetic Data
2005 De Chazal DFT 81%
2005 Zhang Edge Histogram 88%
2006 Pavlou SIFT 85%
2006 Ghouti Wavelet Decomposition 100%
2007 Crookes Phase Only Correlation 82%
2008 Gueham Correlation Filter 94%
2008 Patil Gabor Feature Maps 91%
2008 AlGarni Hu Moments 99%
2008 Pavlou SIFT codebook 92%
2009 Nibouche SIFT + RANSAC 97%
2013 Wei SIFT + Correlation 96%

4. 2006 Ghouti Wavelet Decomposition 100%
Overview Previous Research
Synthetic Data VS Real Case Data
2005 De Chazal DFT 81%
2005 Zhang Edge Histogram 88%
2006 Pavlou SIFT 85%
2006 Ghouti Wavelet Decomposition 100%
2007 Crookes Phase Only Correlation 82%
2008 Gueham Correlation Filter 94%
2008 Patil Gabor Feature Maps 91%
2008 AlGarni Hu Moments 99%
2008 Pavlou SIFT codebook 92%
2009 Nibouche SIFT + RANSAC 97%
2013 Wei SIFT + Correlation 96%

5. = ≠ 2009 Dardi Mahalanobis Map 50% Cervelli Spectral Analysis 17%
Overview Previous Research
Real Case Data
2009 Dardi Mahalanobis Map 50%
Cervelli Spectral Analysis 17%
database not publicly available
no tolerance to rotation, scale, translation = ≠

6. 2009 Dardi Mahalanobis Map 50% Cervelli Spectral Analysis 17%
Overview Previous Research
Real Case Data
2009 Dardi Mahalanobis Map 50%
Cervelli Spectral Analysis 17%
database not publicly available
no tolerance to rotation, scale, translation
2009 Dardi Mahalanobis Map 50%
Cervelli Spectral Analysis 17%
database not publicly available
no tolerance to rotation, scale, translation
2011 Tang Graph Embedding 70%
too strong assumptions ( lines, ellipses, circles )

7. Shoeprint Matching System Overview
Input Data:
Database:
User Interaction:
Rotation
Cropping
Scale Normalization
Mark Outsole
Mark Regions of Interest
Classification:
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching
- Feature Extraction and Matching

8. Input Data - Issues Input Data User Interaction DB Classification
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching
Adapted from Cervelli et al. 2009

9. Over 20’000 crime scene shoeprints and reference prints provided by:
Input Data - Source
Over 20’000 crime scene shoeprints and reference prints provided by:
Test-Database currently contains
220 shoeprints & 1115 reference prints
Still under development 9

10. User Interaction I/II Input Data User Interaction DB Classification
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching

11. User Interaction II/II
Input Data
User Interaction DB
Classification
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching

12. Realignment scale rotation Input Data User Interaction Realignment DB
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching
Classification

13. Classification Results
Cumulative Match Score
220 Shoeprints / 1115 Reference Prints
Input Data
User Interaction
Realignment DB
User Interface: Get rid of those challanges which are easy to solve for a human and make overall task of matching
Classification

14. Overall promising results in the research field
Conclusion
Overall promising results in the research field
Still a variety of challanges
Unconstrained Noise, Photos, Transformation invariance
Need for standard datasets & research projects

15. Questions