Novel Use of GIS for Spatial Analysis of Fingerprint Patterns

1. Novel Use of GIS for Spatial Analysis of Fingerprint Patterns Steve Taylor, Earth and Physical Sciences, Western Oregon University Ryan Stanley, Geology & Geography, West Virginia University Emma Dutton, Forensic Sciences Division, Oregon State Police Pat Aldrich, Natural Sciences and Mathematics, Western Oregon University Bryan Dutton, Biology Department, Western Oregon University Sara Hidalgo, Natural Sciences and Mathematics, Western Oregon University

2. Introduction • GIS Methodology • Example Applications and Results • Discussion and Conclusion

3. NOVEL LINKAGES: GIS AND FINGERPRINT MAPPING So a Geologist, Biologist and Forensic Scientist walk into a bar…the bartender asks: “How are fingerprints like a volcano?” The Geologist says: “I don’t know, but I bet we can use GIS to find out”. The punch line follows… • Fundamental • Map Elements • Points • Lines • Polygons Newberry Volcano

4. Western Oregon UniversityFingerprint Analysis and Characterization Team • “FACT” Interdisciplinary Collaboration: Earth Science, Biology and Forensic Science • Three-year National Institutes of Justice grant- Project Title: “Application Of Spatial Statistics To Latent -Print Identifications: Towards Improved Forensic Science Methodologies”- Project Goal: To apply principles of GIS and spatial analysis to fingerprint characterization

5. PROJECT IMPETUS Feb 2009 National Academy of Science report: “Strengthening Forensic Science in the United States: A Path Forward” Recommendation 3: Indicated need to improve the scientific accuracy and reliability of forensic science evidence, specifically impression-based evidence, including fingerprints

6. Objectives Use Geographic Information Systems spatial analyses techniques to: • Evaluate fingerprint characteristics or attributes • Minutiae type (bifurcations and ridge endings) • Minutiae distribution (per finger / pattern type) • Ridge line distribution • Establish robust probabilistic models to • Quantify fingerprint uniqueness and • Establish certainty levels for latent print comparisons

7. METHODOLOGY

8. FINGERPRINT MORPHOLOGY AND FEATURES IDENTIFICATION: -Minutiae Position -Minutiae Type -Minutiae Direction -Ridge Counts -Ridge “Flow” -Print Type ASSUMPTION: Fingerprints are Biologically Unique Minutiae Points Print Type = LS Friction Ridge Lines Master 1_1li

9. PRIMARY FINGERPRINT TYPES Left Slant Loop Arch Right Slant Loop Whorl

10. Research Design: Application of GIS GIS: A collection of hardware and software that integrates digital map elements with a relational database. Cartography + Database Technology + Statistical Analysis Vector Customers Core to Minutiae Distances and Ridge Counts Streets Parcels Minutiae Elevation Raster Land Usage Fingerprint Skeleton Real World Fingerprint Image Source: ESRI B. GIS Applied to Fingerprints A. Example GIS Application

11. Fingerprint Data Management • Fingerprint image acquisition and minutiae detection • Georeferencing and verification • GIS data conversion and management • Raster fingerprint images • Vector minutiae point layers • Vector friction ridge line layers • Spatial analysis of ridge line and minutiae distributions • Statistical analysis and probability modeling

12. Qualitative Visual Image Assessment Best Good Fair Poor

13. Scan, Segregate & Image Enhancement Noise filter, black/white balance, contrast & brightness enhancements

14. Geo-referencing: Standardized Coordinate System Core Location Arches = highest point of recurve Loops = highest point of recurve of 1st full loop Whorls = center ridge ending or bulls eye Core centered at (100,100) mm in Cartesian space Print placed in NE quadrant with +,+ coordinates

15. GIS Data Conversion Fingerprint Image Fingerprint Minutiae 100 100 100 Skeletonized Ridge Lines 100 100 100 Ridge and Minutiae Attribute Data

16. Fingerprint Skeletonization and Vectorization Coded Ridgeline Attributes

17. FACT Fingerprint Database

18. Project Data Model and Analytical Workflow

19. Example GIS-Based Extension TIN (Delaunay) Triangles 1. Vectorized fingerprint 2. Minutiae 3. TIN polygons 4. TIN polylines 5. TIN ridge counts

20. Custom Python Scripting and Fingerprint Analysis Tools

21. EXAMPLE APPLICATIONS:Pattern Characterization

22. Convex and Detailed Hull Shape Factor = perimeter/(2√(π x area)) LEGEND Bifurcation Ridge Ending Convex Hull Detailed Hull Core Delta Avg. CH = 719.2 mm2 DLW largest CH TA smallest CH

23. 2-mm Grid Cell Minutiae Density All Minutiae 0 0.001 0.01 0.036 0.076 0.14 0.436 n = 348 C. Whorls n = 251 E. Arches n = 54 A. Left Slant Loops B. Right Slant Loops n = 309 D. Double Loop Whorls n = 172 F. Tented Arches n = 66

24. 2-mm Grid Cell Minutiae Density Ridge Endings 0 0.001 0.01 0.036 0.076 0.14 0.436 n = 348 C. Whorls n = 251 E. Arches n = 54 A. Left Slant Loops B. Right Slant Loops n = 309 D. Double Loop Whorls n = 172 F. Tented Arches n = 66

25. 2-mm Grid Cell Minutiae Density Bifurcations 0 0.001 0.01 0.036 0.076 0.14 0.436 n = 348 C. Whorls n = 251 E. Arches n = 54 A. Left Slant Loops B. Right Slant Loops n = 309 D. Double Loop Whorls n = 172 F. Tented Arches n = 66

26. A. Left Slant Loops B. Right Slant Loops C. Whorls D. Double Loop Whorls E. Arches F. Tented Arches 2-mm Grid Cell Ridge Line Density

27. Minutiae / Ridge Frequency Ratio • Above Core • Minutiae: 33 • Ridge Lines: 81 • Minutiae/Ridge Ratio: 0.41 • Below Core • Minutiae: 63 • Ridge Lines: 100 • Minutiae/Ridge Ratio: 0.63 • Compared minutiae / ridge count ratios above and below the core for 188 vectorized fingerprints (all pattern types) • Paired t-test: • t = -24.525, df = 187 • mean difference = -0.19 • p-value < 2.2e-16 • Difference in minutiae / ridge ratios above and below core is significant with a p < 2.2e-16

28. Findings: Pattern Characterization • A robust sample set of over 1200 fingerprints, 102,000 minutiae and 20,000 ridge lines were digitally captured from the Oregon population as part of this project effort • The average number of minutiae per fingerprint is 85.1, with ridge endings outnumbering bifurcations by a factor of 1.4 • Minutiae and ridge lines are most densely packed in the region below the core, with the greatest line-length density surrounding the core • More complex ridge patterns with higher degrees of line curvature (e.g. whorls and double loop whorls) are associated with a greater number of minutiae as compared to more streamlined patterns (e.g. arches).

29. EXAMPLE APPLICATION:Geometric Morphometrics

30. Geometric Morphometrics Figure from Zelditch, M.L., D.L. Swiderski, H.D. Sheets, and W.L. Fink. 2004. Geometric Morphometrics for Biologists: A Primer. Elsevier Academic Press: London. A spatial statistical method to study biological shape Requires the designation of points or areas that are homologous across samples (landmarks and semi-landmarks) Allows shape variation analysis across samples by removing size and rotation effects

31. Morphometrics

32. Generalized Procrustes Analysis (GPA) • Thin Plate Spline RSL Superimposed on LSL Mean Shape Analysis of Landmark/semi-landmark data for 30 Left Slant Loops

33. GPA Superimposition of all landmarks & semi-landmarks from 30 LSL and 30 RSL

34. Findings: Geometric Morphometrics • Using the designated landmarks and semi-landmarks, further analysis of the degree and nature of fingerprint feature variation around putatively homologous regions including deltas, cores, and the single, uninterrupted, edge-to-edge ridgeline distal to the core will be conducted • Analyses will include: • characterization and comparison of the same fingerprint types from left and right hands • investigation of hyper-variable regions of fingerprints outside landmarks/semilandmarks and • an exploration of spatial distortion

35. EXAMPLE APPLICATION:Estimating False Match Probabilities Using Monte Carlo Simulations

36. Monte Carlo and Fingerprints Computer simulations that rely on repeated random samples of a given population Iterative simulation of minutiae is used to obtain a probability of false match Minutiae selected randomly Iterations are founded on searches of entire database False match simulations vary according to the number of attribute criteria selected for the search

37. Monte Carlo Analysis: Comparison of observed minutiae locations to random distributions for determining probabilities of false matches 9 grid cells, each overlapping by 50%, were used as filter boxes to sample the entire print In grid cell, n minutiae were randomly chosen (n = 3,5,7,9) Selected minutiae, located within a positional error tolerance buffer (0.32 mm radius), used to search database for matches Matches consisted of prints that had identical minutiae (x, y) coordinate locations for the prescribed number of minutiae This process was iterated 1000 times per print per grid cell 50 prints for select pattern types (LS Loops, RS Loops, Whorls, Double Loop Whorls) were randomly chosen for analysis yielding a total of 50,000 iterations per grid cell

38. Monte Carlo Simulation: Looking for False matches Legend Fingerprint Convex Hull Ridge Ending Bifurcation Core Delta 120 Grid Cell Grid Cell 7 Grid Cell 8 Grid Cell 9 110 Y Coordinate (mm) Grid Cell Grid Cell 5 Grid Cell 6 Grid Cell 4 100 90 Grid Cell Grid Cell 1 Grid Cell 2 Grid Cell 3 80 110 130 100 70 80 90 120 X Coordinate (mm)

39. Monte Carlo Rubric False positive matches were calculated using the following equation: n=number of fingerprints the minutiae set are compared to j=index of print from which minutiae are drawn i=index of iteration x=number of matches per iteration k=number of prints actually used in the simulation

40. Example False Match – 7 Minutiae, Grid Cell 5 Selected Print: LS Loop – Left Index False Match: Whorl – Left Thumb Matching Minutiae Y Coordinate (mm) Y Coordinate (mm) X Coordinate (mm) X Coordinate (mm)

41. MC1- Coordinates Only 5 Minutiae 3 Minutiae Probability of False Match 1 in 10,000 1 in 1,000,000 1 in 100,000,000 7 Minutiae 9 Minutiae Grid Cell Number

42. MC2- Coordinates + Minutiae Type 3 Minutiae 1 in 10,000,000 5 Minutiae Probability of False Match 1 in 100,000 7 Minutiae 9 Minutiae Grid Cell Number

43. MC6- Coordinates + Print Type + Minutiae Type + Direction 1 in 10,000,000 3 Minutiae 5 Minutiae Probability of False Match 7 Minutiae 9 Minutiae Grid Cell Number

44. Percent Frequency of False Matches • Chances of false matches decrease with increasing number of minutiae sampled

45. Findings: Monte Carlo Simulations • The probability of a false match decreased as the number of parameters increased in the MC model. • The probability of a false match decreased as the number of minutiae increased (without using any other parameters). • The probabilities obtained in this study are aligned with other published results that utilize alternative methods and sample sources.

46. Summary and Conclusion • Techniques in Geographic Information Systems were successfully applied to spatially analyze fingerprint patterns • The georeference protocol developed for this study provides a standardized coordinate system that allows complex analysis of minutiae and ridgeline distributions across fingerprint space • A wide variety of spatial analysis tools were developed in the GIS software environment to characterize fingerprint features and statistically characterize distributions between print types • GIS application to fingerprint analysis, identification and pattern characterization represents an untapped resource • The project-related GIS tools and preliminary results offer promising contributions to the advancement of fingerprint analysis and forensic science in the near future.

47. FUTURE WORK • Apply rubber sheeting and ortho-rectification techniques to elastic skin deformation associated with traditional rolled-ink standards and latent print collection techniques • Conduct Nearest Neighbor simulations using randomly chosen clusters of minutiae • Refine the Monte Carlo simulations to capture probabilities of false matches at higher minutiae counts • Expand the project database to include fingerprint samples beyond the existing Oregon data set • Standardize the GIS tools and data framework

48. Acknowledgements • National Institute of Justice (Grant Award # 2009-DN-BX-K228) • Western Oregon University • Oregon State Police, Forensic Services Division and ID Services Division • Undergraduate and Graduate Student Assistants This project was supported by Award No. 2009-DN-BX-K228 awarded by the National Institute of Justice, Office of Justice programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect those of the Department of Justice.