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 Services 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
• Introduction
 Project Background
• GIS Methodology
 Data Model
 Standardized Coordinate System
 Workflow
• Example Applications
 Pattern Characterization
 Geometric Morphometrics
 Monte Carlo Simulations
• Summary and Conclusion
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’m not sure, but I bet we can use GIS to find out”. The punch line follows…
Fundamental
Map Elements
• Points
• Lines
• Polygons
Newberry Volcano
Morphometric Group II
(Morphology Rating
Classes 4, 5, 6, and 7)
Fingerprint Spatial
Data in a GIS
Raster Data
Fingerprint
Images
Points
Fingerprint
Minutiae
Lines
Fingerprint
Ridges
Polygons
Fingerprint
Convex Hulls
Moprhometric Group I
(Morphology Rating
Classes 1, 2, and 3)
Newberry
Caldera
0
0
3
6
9
12
Millimeters
5 km
Western Oregon University
Fingerprint Analysis and Characterization Team
“FACT” Interdisciplinary Collaboration:
Earth Science, Biology and Forensic Science
Three-year National Institute 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
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
Objectives
Use Geographic Information Systems spatial
analyses techniques to:
• Evaluate fingerprint characteristics or attributes
1. Minutiae type (bifurcations and ridge endings)
2. Minutiae distribution (per finger / pattern type)
3. Ridge line distribution
• Establish robust probabilistic models to
1. Quantify fingerprint uniqueness and
2. Establish certainty levels for latent print comparisons
METHODOLOGY
Master1_1li
FINGERPRINT
MORPHOLOGY AND FEATURES
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Core
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IDENTIFICATION:
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Delta
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-Minutiae Position
-Minutiae Type
-Minutiae Direction
-Ridge Counts
-Ridge “Flow”
-Print Type
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Ridge Ending
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Bifurcation
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ASSUMPTION:
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Fingerprints are
Biologically
Unique
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Print Type = LS
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Minutiae Points
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Friction Ridge
Lines
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5 Millimeters
Master 1_1li
PRIMARY FINGERPRINT TYPES
Right Slant Loop
Left Slant Loop
Arch
Whorl
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
V
e
c
t
o
r
Customers
R
a
s
t
e
r
Elevation
Core to Minutiae Distances
and Ridge Counts
Streets
Parcels
Source: ESRI
Minutiae
Land Usage
Fingerprint Skeleton
Real World
Fingerprint Image
A. Example GIS Application
B. GIS Applied to Fingerprints
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
Scan, Segregate & Image Enhancement
• Noise filter, black/white balance, contrast & brightness enhancements
Master1_1li
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180
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Azimuth Orientation
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25
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100 mm
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50
0
75
100
Y Coordinate (mm)
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90
150
100 mm
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270
125
Geo-referencing: Standardized
Coordinate System
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• Core Location
0
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5
0
5 Millimeters
0
25
Arches = highest point of recurve
50
75
100
X Coordinate (mm)
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 oriented with basal crease parallel to X-axis
125
150
Fingerprint Image
100
GIS Data Conversion
Fingerprint Minutiae
100
100
100
Skeletonized Ridge Lines
Ridge and Minutiae
Attribute Data
100
100
X_COORD Y_COORD
MIN_DIR
PT_ID MIN_TYP
100
100
180
1000 C
PRNT_TYP
RS
File_Id
1_87_ri
96.4199982
95.12
-1
2001 D
RS
1_87_ri
88.4899979
92.7857437
45
1 A
RS
1_87_ri
88.8700027
92.36
225
2 B
RS
1_87_ri
89.1500015
100.05
214
3 B
RS
1_87_ri
89.6100006
88.8071796
68
4 A
RS
1_87_ri
90.5
90.4757437
56
5 A
RS
1_87_ri
90.8300018
89.18
236
6 B
RS
1_87_ri
91.1999969
88.75
68
7 A
RS
1_87_ri
91.3499985
93.9671282
45
8 B
RS
1_87_ri
Fingerprint Skeletonization and Vectorization
Core
Coded
to Ridgeline
Delta Ridge
Attributes
Count
RidgesRidge Ending
16 - Ridge Ending
Ridge Ending - Bifurcation
11.11 mm
Ridge Ending - Hull
Bifurcation
- Bifurcation
Density
1.53
ridges/mm
Bifurcation - Hull
Hull - Hull
Distance
Line
Ridge Ending - Ridge Ending
Bifurcation - Bifurcation
FACT Fingerprint Database
FINGER
Table 1. Frequency of Pattern Type by Finger and Hand
Double
Left
Right
Tented
Loop
Whorl
Arch
Loop
Loop
Arch
Whorl
TOTAL
Left Index
125
45
28
58
18
30
304
Right Index
48
110
15
78
21
36
308
Left Thumb
173
2
66
41
9
0
291
2
152
63
74
6
0
297
348
309
172
251
54
66
1200
Left
Loop
Right
Loop
Double
Loop
Whorl
Whorl
Arch
Tented
Arch
TOTAL
298
47
94
99
27
30
595
Right Thumb
TOTAL
HAND
Left Hand
Project Data Model and Analytical Workflow
100 - Data Collection Methods
Image Database Entry
Core-to-Minutia
Point-to-Point Digitization
Delta-to-Minutia
Point-to-Point Digitization
Minutia-to-Minutia
Point-to-Point Digitization I
(w/o Core + Delta)
Minutia-to-Minutia
Point-to-Point Digitization II (with Core +
Delta)
Ridge Counts
Ridgeline Skeletonization
Core-Only Point Layer
Delta-Only Point Layer
Convex & Detailed Hull
Bounding Polygons
Axis Layer (Longitudinal/Transverse)
Minutiae Buffers
Coded Ridgelines
Landmark/Semilandmark Designation
200 - Pattern Characterization
Methods
Dart Board Min-Point
Frequency-Density Quadrat
Thiessen Polygons I
(Clipped to Hull)
Thiessen Polygons II
(Dissolved by Min-Type)
TIN Polygons
Min-Point Frequency
Density Quadrat
(2 mm Grid)
Ridge Line Frequency Density Quadrat
(2 mm Grid)
Ridge Line Frequency Density TIN-Based
& Thiessen Polygon-Based
Superimposition
300 – Statistical/Probability
Modeling Methods
Minutiae Azimuth Frequency
Histograms
Minutiae Azimuth Frequency
Rose Diagrams
Radar Plot
Minutiae Positions
(azimuth vs. dist. from core)
Nearest Neighbor Analysis
Principle Components Analysis (PCA)
Generalized Procrustes Analysis (GPA)
Thin-Plate Spline (TPS) Deformation
Modeling
Example GIS-Based Extension
TIN (Delaunay) Triangles
2
20
2
8
5
7
1. Vectorized fingerprint
5
2. Minutiae
3. TIN polygons
2
11
2
4
3
4
7
3
4
1
6
0 3
1
1
2 3
4
2
0 11 1
0
2
0 1 0 001
0
3
1
1
12
0 3
3
0
2
2
20 1 3
4
1
1
9
20
8
8
1
5
3
2
2
2
1
3
2
4
2
2
3
2
5 34
9
3
2
3 4
1
2
4 2
0 1
8
0
5
0
2
0 2 1
0
12 1
2
0 22
0
0
4
1
1
2
0
01
1
7
2
4
1
2
1
1 2 2
0
0
3
3
2
2 3
0
1
2
1
3
1
6
2
1
1
2
2
1
1
4
3
0
3
4
0
1
3
1
2
0
2
1
3
4
4
4 13
5
11
1
5
11
13
2
7
2
1
1
1
11
3
0
0
2
3
33
0
1
1
4
0
8
1
2
1
3
3
3
2
0
5
2
4
7
4
5
43
2
4
3
6
1
7
1
1
1
0
0
1
6
9
2
2
1
0
2
0
21
1
2
1
6
0
53
11 2 4
3
2
3
1
0
2
1
3
0
44
2
5. TIN ridge counts
2 4
3
7
2
4. TIN polylines
8
6 4
7
3
4
7
6
1
4
5
7
7
1
1
1
4
4 4
7
4
0
5
2
3
1
2
4
0
13
3 2
Custom Python Scripting and Fingerprint Analysis Tools
EXAMPLE APPLICATIONS:
Pattern Characterization
B. Right Slant Loops
120
6
.4
3
.1
4
76
-0
1
0.
14
61
0.
07
03
61
-0
.0
36
-0
01
01
-0
110
120
n = 66
80
90
100
110
120
110
100
F. Tented Arches
n = 172
110
100
0.
120
100
90
0.
120
110
90
80
90
100
100
80
80
90
100
110
120
90
D. Double Loop Whorls
n = 309
80
n = 54
80
80
120
0.
110
0.436
120
100
0.14
90
100
90
80
90
E. Arches
0.076
110
120
n = 251
110
120
110
100
90
80
80
00
C. Whorls
n = 348
A. Left Slant Loops
0.036
.0
0.01
.0
1
-0
0.001
1
0
0
Average Minutiae Density
(Avg. Number Minutiae / Sq. mm)
2-mm Grid Cell Minutiae Density
All Minutiae
80
90
100
110
120
80
90
100
110
120
2-mm Grid Cell Ridge Line Density
Avg. Minutiae
Density
(minutiae
/ sq.
Ridge
line density
(total
length
in mm)
mm/sq. mm)
00
C. Whorls
110
B. Right Slant Loops
110
120
80
120
120
100
110
120
120
110
100
110
90
100
80
90
110
90
F. Tented Arches
80
100
110
120
100
120
120
110
100
90
90
100
90
90
D. Double Loop Whorls
80
80
0.14
58.0
80
80
120
0.076
30.0
100
100
90
80
100
0.036
15.0
110
110
120
110
100
90
80
90
0.01
3.0
E. Arches
120
A. Left Slant Loops
80
0.001
0.000001
80
90
100
110
120
80
90
0.45
81.0 <
Minutiae / Ridge Frequency Ratio
• Compared minutiae / ridge count
ratios above and below the core
for 188 vectorized fingerprints (all
pattern types)
Above Core
- Minutiae: 33
- Ridge Lines: 81
- Minutiae/Ridge Ratio: 0.41
• 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
Below Core
- Minutiae: 63
- Ridge Lines: 100
- Minutiae/Ridge Ratio: 0.63
Findings: Pattern Characterization
•
•
•
•
•
Project Compilation:
 1,200 fingerprints
 102,000 minutiae
 20,000 ridge lines
Avg. No. Minutiae per Print = 85.1
Ridge Ending/Bifurcation Ratio = 1.4
Minutiae and ridge lines most densely packed in the region
below the core, with the greatest line-length density
surrounding the core
Increased ridge line curvature associated with increased
minutiae density
EXAMPLE APPLICATION:
Geometric Morphometrics
Geometric Morphometrics
• A spatial statistical method to
study biological shape
• Requires the designation of
points or areas that are
homologous across samples
(landmarks and semilandmarks)
• Allows shape variation
analysis across samples by
removing size and rotation
effects
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.
Figure 1: Inputs and template features used in landmark extraction procedure
Fingerprint Morphometrics
Figure 1A
Figure 1B
Example Left Slant Loop
Legend
Landmarks
Innermost Recurving Core Loop
Continuous Ridge
Fingerprint Convex Hull
Core to Continuous Ridge Template
Core to Delta Loop Template
Delta Region Template
Findings: Geometric Morphometrics
• Geometric morphometric techniques are applicable to
fingerprint patterns
• Potential Research Directions:
Geometric comparison of fingerprint types between
left and right hands
Analysis of hyper-variable regions of fingerprints
outside landmarks and semilandmarks
Analysis of the effects of elastic skin deformation
and spatial distortion in fingerprints
EXAMPLE APPLICATION:
Monte Carlo Simulation and Estimating
False Match Probabilities
Monte Carlo Simulation
• Iterative random sampling of select minutiae to obtain probabilities of
false matches based on coordinate location and point attributes
• 9 grid-filter cells, each overlapping by 50% across entire print space
• 3-5-7-9 minutiae systematically sampled in each grid cell
• Simulation iterated 1000 times per print per grid cell
• 50 prints selected across four pattern types (LS Loops, RS Loops,
Whorls, Double Loop Whorls) yielding a total of 50,000 iterations per
grid cell
Monte Carlo Simulation:
Looking for False matches
Legend
Fingerprint Convex Hull
Ridge Ending
Bifurcation
120
100
110
Grid Cell 9
7
8
90
Grid Cell 5
6
4
90
(mm)
Y Coordinate
100
110
Grid Cell 3
1
2
80
80
Delta
120
Core
70
70
80
80
90
90
100
100
110
110
XX Coordinate
Coordinate(mm)
(mm)
120
120
130
130
Example False Match – 7 Minutiae, Grid Cell 5
Selected Print:
LS Loop – Left Index
115
110
Y Coordinate
100 (mm)
105
95
90
85
80
80
85
90
95
100
Y Coordinate (mm)
105
110
115
False Match:
Whorl – Left Thumb
90
95
100
105
X Coordinate (mm)
110
115
90
Matching Minutiae
95
100
105
X Coordinate (mm)
110
115
Findings: Monte Carlo Simulations
• The probability of a false match decreased as the
number of selection attributes increased in the MC
model.
• The probability of a false match decreased as the
number of selected minutiae increased.
• The probabilities obtained in this study are aligned
with other published results that utilize alternative
methods and sample sources.
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.
FUTURE WORK
• Apply rubber sheeting and ortho-rectification
techniques to elastic skin deformation associated
with traditional analog print collection techniques
• Conduct Nearest Neighbor false-match simulations
using randomly chosen clusters of minutiae
• Refine Monte Carlo simulations to capture falsematch probabilities 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
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.