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Israel Police, Division of Identification and Forensic Science. • Toolmarks and Materials Lab

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Israel Police, Division of Identification and Forensic Science. • Toolmarks and Materials Lab Israel Police, Division of Identification and Forensic Science. • Toolmarks and Materials Lab. • Hadassah Academic College, Dept. of Computer Science • Hebrew University, Dept. of Statistics. The Israeli research team started 15 years ago, after a European “scale committee” was established. Vienna, 2003, Conclusion scale committee A small cut (1 mm) in the sole, how significant can it be? 97/3 point 1 Marks Working Group - Scale committee meeting Vienna - Finnish tests It is not the size… Changing angle 2 mm Is it a 1, 3 or 5 component? - it’s amount of information… Characterizing Shoeprints – From Class Characteristics To Accidental Marks Sarena Wiesner1 M.Sc., Yaron Shor1 M.Sc., Yoram Yekutieli2 Ph.D. 1) Toolmarks and materials lab. DIFS, Israel police 2) Hadassah Academic College, Dept. of Computer Science, Jerusalem. SAMSI workshop September 1st 2015 Technical Report TP-3211, Submitted to the U.S. Department of Justice, NIJ. 5 SAMSI September 1st, 2015 How do we Identify shoes? 6 SAMSI September 1st, 2015 Fingerprints vs. Shoeprints Fingerprints • Can’t be changed. • Consistent throughout whole life. • Typical pattern. • Minutiae consist of two basic types. • Spatial configuration of minutiae determines individuality. 7 SAMSI September 1st, 2015 Fingerprints vs. Shoeprints Shoeprints • Life span several years. • People own several pairs. • Changes with wear. • Unlimited patterns. • Unlimited accidental shapes. 8 SAMSI September 1st 2015 Connecting shoe to shoeprint Class characteristics • Pattern • Size • Wear Accidental characteristics • Cuts • Nicks • Tears • Foreign objects 9 SAMSI September 1st, 2015 Pattern Pattern is not brand! Reebok 10 SAMSI September 1st, 2015 Size 7.5 9 10.5 12 11 SAMSI September 1st, 2015 Wear Slight wear Intermediate wear Extreme wear 12 SAMSI September 1st, 2015 Accidental characteristics / Randomly acquired characteristic A feature …resulting from random events including, but not limited to: cuts, scratches, tears, holes, stone holds, abrasions and the acquisition of debris. The position, orientation, size and shape of these characteristics contribute to the uniqueness of a footwear outsole or tire tread. (SWGTREAD - Standard for Terminology Used for Forensic Footwear and Tire Impression Evidence) 13 SAMSI September 1st, 2015 Sampling process – Case work Suspect shoe Same accidentals Same Yes Test Same size appear on & wear? pattern? impressions print & shoe? Crime scene No No print Determine Exclusion Exclusion level of certainty 14 SAMSI September 1st, 2015 Creating test impressions 15 SAMSI September 1st, 2015 Sampling process – Case work Shoe Same accidentals Same Yes Test Same size appear on & wear? pattern? impressions print & shoe? Print No No Determine Exclusion Exclusion level of certainty 16 SAMSI September 1st, 2015 Comparison process 17 SAMSI September 1st, 2015 Comparison process • Very high quality prints – can see accidentals without shoe. • Most cases – See accidentals with overlaid test impression. • Very low quality prints – look at shoe to identify what to search for on print. 18 SAMSI September 1st, 2015 Sampling process – Case work Shoe Same accidentals Same Yes Test Same size appear on & wear? pattern? impressions print & shoe? Print No No Determine Exclusion Exclusion level of certainty 19 SAMSI September 1st, 2015 Determining level of certainty Identification Highly Probable Probable Possible Can’t be eliminated 20 NegativeSAMSI September 1st, 2015 From case work to research • Class characteristics not very discriminative. • Shoe-sole patterns change often. Focus on • Tearing behavior of accidentals! shoe-sole material similar for most shoes. 21 SAMSI September 1st, 2015 Database consisting shoes of various patterns A universal system for any possible pattern. Enables creating large database. Most shoe soles made of similar material, hence tear in similar way. Only contact areas can contain accidentals. Week points based on pattern. 22 SAMSI September 1st, 2015 Sampling process – Creating database of accidentals • Creating test impressions. • Scanning test impressions. • Semi-Automatically marking accidentals. • Finding shoe aligned coordinate system. 23 SAMSI September 1st, 2015 Scanning test impressions • 600 ppi. • Color. • JPG or TIFF files. 24 SAMSI September 1st, 2015 Semi-Automatically marking accidentals Marking the area Automatic marking Fine tuning the of the accidental of borders threshold Cleaning excess markings 25 SAMSI 2015st, 1September Three Criteria to evaluate an accidental Orientation 26 SAMSI September 1st, 2015 All the assumptions used are initial assumptions and further investigation into them is required! 27 SAMSI September 1st, 2015 The probability of having an accidental in a specific location Basic assumptions: We divide the test impression into grid cells and assume no more than one accidental in each cell. The size of each grid cell is the size of the error in locating the accidentals. Error ≈(5 mm) 2 = 0.25 cm2. The area of a shoe is 30x10 cm2 = 300 cm2 Assuming a uniform distribution of locations: p=1/1200 Average of 30 accidentals per shoe: P= 30 * (1/1200) = 1/40 28 SAMSI September 1st, 2015 Ref: Yoram Yekutieli, “Challenges in image analysis of shoeprints”, Tomorrow 9:45 The probability for a specific accidental orientation Basic assumptions: • The orientation of a shape is the angle of its major (longer) axis relative to the X axis of the shoe aligned coordinate system. • Error rate is determined by degree on elongation. • The distributions of orientations are largely uniform. 29 SAMSI September 1st, 2015 Ref: Yoram Yekutieli, “Challenges in image analysis of shoeprints”, Tomorrow 9:45 The probability of having an accidental with a specific shape Accidental shape compared against all the accidentals in the database. 30 SAMSI September 1st, 2015 Ref: Yoram Yekutieli, “Challenges in image analysis of shoeprints”, Tomorrow 9:45 The probability of having an accidental with a specific shape • For each comparison calculating its matching error. • Counting how many comparisons had a matching error value lower than the threshold. • Assuming 500 out of the 10,000 shapes lower than threshold: p(shape) = 500/10,000 = 1/20 31 SAMSI September 1st, 2015 Ref: Yoram Yekutieli, “Challenges in image analysis of shoeprints”, Tomorrow 9:45 Total probability of a single accidental Assuming independence of the parameters, for one accidental we have: p(accidental) = p(location) * p(orientation) * p(shape) = 1/40 * 1 / 18 * 1/6752 = 2.06e-7 32 SAMSI September 1st, 2015 Probability of a combination of accidentals We assume the orientations are independent. We assume that their shapes are independent. We assume the locations are independent. p(combination) = p(3 locations) * p(3 orientations) * p(3 shapes) And this is the same as ( p(single accidental) 3 ) * 3! Ref: Yoram Yekutieli, “Challenges in image analysis of shoeprints”, Tomorrow 9:45 33 SAMSI September 1st, 2015 The SESA system 34 SAMSI September 1st, 2015 Location - Checking the assumptions Is it justified to assume one accidental per cell? 35 SAMSI September 1st, 2015 Orientation Checking the assumptions How accurate is determining the orientation error based on the degree of elongation? 36 SAMSI September 1st, 2015 Shape - Checking the assumptions • Probability of rare accidentals limited by No. of accidentals in database. • Texture of shoe may interfere with correctly determining the shape contour. 37 SAMSI September 1st, 2015 Total probability Checking the assumptions • Is there any dependence between location, orientation and shape of an accidental? • Some accidentals are pattern element dependent! • Is there any dependence between different accidentals? 38 SAMSI September 1st, 2015 How does the working procedure affect the reliability of the database? Present comparison method Former comparison method 39 SAMSI September 1st, 2015 Test impression scanning method Former scanning method Present scanning method 40 SAMSI September 1st, 2015 Size of accidentals found with the new scanning and comparison method 41 SAMSI September 1st, 2015 From theory to reality 42 SAMSI September 1st, 2015 Real life shoeprints Test impression Print from crime scene 43 SAMSI September 1st, 2015 Real life accidentals • Partial shoeprint 44 SAMSI September 1st, 2015 Real life accidentals • Twisted prints 45 SAMSI September 1st, 2015 Real life accidentals • Partial accidental 46 SAMSI September 1st, 2015 Real life accidentals • Noise 47 SAMSI September 1st, 2015 Real life accidentals • Vague resemblance 48 SAMSI September 1st, 2015 Presenting the conclusion at court How do we translate the derived probabilities to an understandable and reliable conclusion? 49 SAMSI September 1st, 2015 ENFSI Marks conclusion scale Level Likelihood ratio Probability 1 Extremely strong support for 1,000,000 and above Identification proposition A 2 Very strong support for proposition 10,000 – 1,000,000 Very probably A Strong support for proposition A 1000 – 10,000 3 Moderately strong support for 100 – 1000 Probably proposition A Moderate support for proposition A 10 – 100 Weak support for proposition A 2 - 10 4 Inconclusive 1 Inconclusive 5 Weak support for proposition likely not (=not A) Moderate support for proposition Moderately strong support for proposition Strong support for proposition Very strong support for proposition 506 Elimination SAMSI Elimination 2015st, 1September Probability based on SESA system 51 SAMSI September 1st, 2015 Distribution of probabilities per levels of certainty Identification Very probably
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