A comprehensive research on shoeprints RAC’s

1. A comprehensive research on shoeprints RAC’s Impression, Pattern and Trace Evidence Symposium (IPTES) Arlington, VA, Jan. 25, 2018 YaronShor M.Sc. SarenaWiesnerM.Sc.YoramYekutieliPh.D, Alicia Carriquiry Ph.D., Micha Mandel, Ph.D, Naomi Kaplan-Damri M.A IPTES, Impression, Pattern and Trace Evidence Symposium

2. “Individualization” out, “Amount of information” - in! “one good RAC is enough to determine “identification” (Bodziak, shoeprints etc….2000) PCAST, 2016: “The entire process—from choice of features to include (and ignore) and the determination of rarity—relies entirely on an examiner’s subjective judgment. IPTES, Impression, Pattern and Trace Evidence Symposium

3. Furthermore: “… the committee is not aware of any data about the variability of class or individual characteristics… Without such population studies, it is impossible to assess the number of characteristics that must match in order to have any particular degree of confidence about the source of the impression.” National Academy of Sciences, Strengthening Forensic Science in the United States: A Path Forward, 2009, IPTES, Impression, Pattern and Trace Evidence Symposium

4. What they all mean is that “how can you quantify the information, so we can all see and be convinced from your PRACTICAL experience?” We started in looking for a way to find the amount of information an a fractured line. IPTES, Impression, Pattern and Trace Evidence Symposium

5. Torn Silicon and Contour Representation IPTES, Impression, Pattern and Trace Evidence Symposium

6. Photograph of 1 cm comparison of metal-coated paper Erroneous match of another area in the wrong strip Correct photograph match IPTES, Impression, Pattern and Trace Evidence Symposium

7. Computer Comparison of a Small Segment Against the Data Base. IPTES, Impression, Pattern and Trace Evidence Symposium

8. The Research Process • Creating the data bases. • Running computer comparisons of small segments against the whole data base. • Dividing the comparison results in two: Matches and Mismatches. • Deriving statistics and error rates for each material data base. • (The project was sponsored by the NIJ, Task no, XX) IPTES, Impression, Pattern and Trace Evidence Symposium

9. Amount of information • Every piece of information is relevant! • The source of information can be internal (within the examined shoe) or external (in the brand or nation database). • Avoid numbers! IPTES, Impression, Pattern and Trace Evidence Symposium

10. Shoeprints RAC’s: Pictorial library The simplest way: an album with ALL the RAC’s ever seen. IPTES, Impression, Pattern and Trace Evidence Symposium

11. Shoeprints RAC’s: The Dutch guidelines • Sort the RACs by size and complexity. • Give an hypothetical value for each RAC • Sum up the cumulative value of all the RACs • Build a scale to show the rareness of such a combination IPTES, Impression, Pattern and Trace Evidence Symposium

12. Sorting the RACs by size & complexity 2H H1 H2 M1 M2 H1 H2 IPTES, Impression, Pattern and Trace Evidence Symposium

13. Hypothetical confidence curve IPTES, Impression, Pattern and Trace Evidence Symposium

14. Similarity 1 IPTES, Impression, Pattern and Trace Evidence Symposium

15. IPTES, Impression, Pattern and Trace Evidence Symposium

16. Shoeprints RAC’s Speir JA work: processing a collection of more than 1,000 shoes and 57,426 randomly acquired characteristics. (https://www.researchgate.net/scientific-contributions/2051690211_Jacqueline_A_Speir) IPTES, Impression, Pattern and Trace Evidence Symposium

17. SESAStatistical Evaluating of Shoeprints Accidentals Sponsored by: NIJ, task no. 3211 IPTES, Impression, Pattern and Trace Evidence Symposium

18. SESA: a semi-automatic way. IPTES, Impression, Pattern and Trace Evidence Symposium The human operator marks the edge of the shoe, and defining the center of the upper sole- determining the beginning of axes. The software marks the border of the RAC’s shape and its relative location on the sole The human operator check and correct the results. The RAC gets an “electronic”, objective representation and is stored in the DataBase

19. SESA: how its being done IPTES, Impression, Pattern and Trace Evidence Symposium • “MarkAccidentals” takes as input a scan (600 dpi) of a test impression, and marks the contour and location of each accidental as chosen by the operator. • The accidentals are kept automatically in the Database. • All the observed (or desired)accidentals on the test impression are marked.

20. The procedure: (1) Scanning IPTES, Impression, Pattern and Trace Evidence Symposium

21. 2. Focusing on one RAC IPTES, Impression, Pattern and Trace Evidence Symposium

22. 3. Marking the borders IPTES, Impression, Pattern and Trace Evidence Symposium

23. 4. Fine tuned- final shape to Compare IPTES, Impression, Pattern and Trace Evidence Symposium

24. All the marked accidentals:1. Calculate rarity for each RAC2. Calculate the combination IPTES, Impression, Pattern and Trace Evidence Symposium

25. CompareAccidentals: against DB IPTES, Impression, Pattern and Trace Evidence Symposium

26. CompareAccidentals: against DB IPTES, Impression, Pattern and Trace Evidence Symposium

27. Limitations? • 400 different shoes, models and materials • The shoes were collected from real cases, with uncontrolled wear degree. • Various sizes: marking location on “artificial” shoe • Contact area with surface is different for each model • Not suitable for “black box” tests • Very difficult to analyze dependencies between variables IPTES, Impression, Pattern and Trace Evidence Symposium

28. The new research CSAFE (and Iowa State University) with the DIFS (Israel Police), the Hebrew University in Jerusalem and Hadassa College started a new research: Analysis and interpretation of shoeprint evidence The overall goal of this research is to increase the usefulness of footwear evidence as a forensic tool. IPTES, Impression, Pattern and Trace Evidence Symposium

29. Goals of the project • Expand the existing dataset of lab prints of shoes. • Identify a highly discriminatory set of features that can be used to describe accidentals. • Propose a statistical model to describe the joint distribution of accidentals • Begin exploring the critical issue of uniqueness and repeatability of shoe print signatures • Understand the robustness of the methods described above for noisy shoe prints collected from crime scenes • Develop a full statistically defensible analytical approach to shoeprint identification and matching incorporating the components described above. IPTES, Impression, Pattern and Trace Evidence Symposium

30. The new project outlines • 120 volunteers wearing two sizes only (8 and 10). • Only two shoe pattern sole- good source of data • Each shoe is scanned and test impression is taken every month. Checking the wear and RACs condition on the shoes can give tremendous amount of information. • The RACs will be digitized in the DIFS. IPTES, Impression, Pattern and Trace Evidence Symposium

31. The future: CSAFE and the Forensics • In the future: making “scene of crime” with the research shoes, and checking “real life” results. • “Black box” tests becoming available. • The data will be processed at CSAFE looking for dependencies, correlation between variables and the degree of certainty for each RAC IPTES, Impression, Pattern and Trace Evidence Symposium

32. The pilot research In the DIFS and the Hebrew University in Jerusalem a pilot plan is running with 10 same size and model shoes. All the shoes were distributed on October 2017 The soles were scanned and test impression were made. 15 days later we checked the shoes. 45 days later some RACs were present and the different wear conditions were observable. IPTES, Impression, Pattern and Trace Evidence Symposium

33. 10 Different soles after 45 days IPTES, Impression, Pattern and Trace Evidence Symposium