Dakota Boyd Dustin Short Elizabeth Lee John Huppenthal Shelby Proft Wacey Teller History of Fingerprinting Originally used paper and ink fingerprints Fingerprints were matched using trained individuals ID: 683534
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Slide1
Statistics of Fingerprints
Dakota Boyd, Dustin Short, Elizabeth Lee, John
Huppenthal
, Shelby
Proft
,
Wacey
TellerSlide2
History of Fingerprinting
Originally used paper and ink fingerprints
Fingerprints were matched using trained individuals
Initially, each country has its own standardsDigital fingerprinting lead to international standardsFingerprints can now be matched or partially matched using algorithms
Section 6.1-6.3 from
The Fingerprint SourcebookSlide3
Problems with Automated Fingerprint Processing Systems
Digital Fingerprint acquisition
Image enhancement
Feature/Minutiae extractionMatchingIndexing/retrieval
Section 6.4.1 from
The Fingerprint SourcebookSlide4
Fingerprint Acquisition
Ink and paper method
Latent prints
Livescan images – fingerprint sensorsFTIR optical scanner
Capacitive scanner
Piezoelectric scanner
Thermal scanner
Figure 6-6 from
The Fingerprint SourcebookSlide5
Image Enhancement
Many acquisition types leads to many noise characteristics
Enhancement algorithms help correct unwanted noise
Latent Print Enhancement
Automated Enhancement
Figure 6-10 from
The Fingerprint SourcebookSlide6
Feature Extraction
Binarization
algorithm – Black is ridges, white is valleys
Thinning algorithm leads to the thinned image or skeletal imageMinutiae detection algorithm locates the x
,
y
, and theta coordinates of the minutiae points
Minutiae post processing algorithm to detect false minutiae
Section 6.4.4 from
The Fingerprint SourcebookSlide7
Matching
Factors that influence matching from fingerprint acquisition: displacement, rotation, partial overlap, nonlinear distortion, pressure, skin conditions, noise from imaging, errors from feature extraction
First need to establish alignment
Programs may use core and delta points to align fingerprints
Could use Hough transform
Then match minutiae
Fingerprint is then given a matching score
High = high probability fingerprint are a match
Low = low probability the fingerprints are a match
Section 6.4.5 from
The Fingerprint SourcebookSlide8
Indexing Fingerprints
Need to be able to index and retrieve fingerprints of a given individual
Before digital fingerprints, forensic experts used filing cabinets to organize prints using a classification system
Prints are explicitly classified by overall shape: right loop, left loop, whorl, arch, tented arch, and double loop
Can be continuously classified using vectors
Section 6.4.6 from
The Fingerprint SourcebookSlide9
The Galton Model
First probability model for fingerprint individuality (1892).
Variously sized square papers dropped over sections of a fingerprint, and a prediction of whether or not the paper cover minutiae.
Model not based on actual distribution or frequency of minutiae.Estimated probability of different pattern types present and the number of ridges in the selected region of the print.
Probability of finding any given minutiae in a fingerprint given as 1 in 68 billion.Slide10
The Osterburg
Model
1977-1980
Divide fingerprint into 1 sq. mm sections and count the occurrence of 13 different minutiae appearances in each section.Rarity of a fingerprint arrangement = product of all individual minutiae frequencies and empty cells.
Example: 72 sq mm fingerprint, 12 ridge endings, each in one cell, 60 empty cells, probability = (0.766)
60
(0.0832)
12
= 1.25
x
10-20. 0.766 and 0.0832 are
Osterburg’s
observed frequencies of an empty cell and a ridge ending.
Problem: This model assumes each cell/section event is independent.Slide11
The Stoney
and Thornton Model
Classifying Characteristics
Ridge structure and description of minutiae locations.Descriptions of minutia distribution.
Orientation of minutiae.
Variation in minutiae types.
Variation among prints from the same source.
Number of orientations and comparisons.
1985-1989
Determined criteria for an ideal model to calculate individuality of a fingerprint and the probabilistic strength of a match.
Each minutiae pair is described by the six characteristics and the spatial position of the pair within the entire fingerprint.Slide12
The
Pankanti
,
Prabhakar, and Jain Model2001Model assesses probabilities of false matches, not individuality of fingerprints.
Calculates the number of possible arrangements of ridge endings and bifurcations.
Calculated spatial differences of minutiae in pairs, and accept similar spatial calculations as matches. (
x
,
y
,
θ).
Each fingerprint had four captures, separated in two databases, to determine an acceptable tolerance of error based on natural variations.Slide13Slide14
First Level Detail
Direction of ridge flow in the print.
Not necessarily defined to a specified fingerprint pattern.
General direction of ridge flow is not unique.Slide15
Second Level Detail
Pathway of specific ridges.
Includes starting position, path of the ridge, length, and where the ridge path stops.
Includes configurations with other ridge paths.Uniqueness is found with the ridge path, length, and terminations.A general direction must exist (first level detail).Slide16
Third Level Detail
Shapes of the ridge structures.
Morphology of the ridge: edges, textures, and pore positions on the ridge.
Shapes, sequences, and configurations of third level detail are unique.General direction (first level) and a specific ridge path (second level) must exist for third level detail.Slide17Slide18
Persistence
Comparing the visibility of minutiae in fingerprints over a time span.
Galton found one discrepancy, where a single bifurcation was not present 13 years later.
Other studies with age spans ranging up to 57 years found no discrepancies of minutiae.All in first and second level detail.Slide19
Persistence
Pores on the ridges of friction ridge skin remain unchanged throughout life. Their location remains the same.
Palm creases (third level detail of the palm) have seen changes over long time periods.
Due to age of the skin, skin flexibility, and other factors. All in third level detail.Slide20
Persistence
Basal layer (regenerative layer between dermis and epidermis).
Friction ridge skin persistency is maintained by the regenerative cells in the stratum
basale, and the connective relationship of these cells.Slide21
Examination Method
Analysis, comparison, evaluation (ACE) and verification (V)
This is one description of a method of comparing details, forming a hypothesis about the source, experimenting to determine whether there is agreement or disagreement, analyzing the sufficiency of agreement or disagreement, rendering an evaluation, and retesting to determine whether the conclusion can be repeated.Slide22
Examination Method
Analysis
The assessment of a print as it appears on the substrate.
Makes the decision of whether the print is sufficient for comparison with another printLooks at the substrate, matrix, development medium, deposition pressure, pressure and motion distortion, and development medium for appearance and distortionSlide23
Examination Method
Comparison
Determine whether the details in two prints are in agreement based upon similarity, sequence, and spatial relationship occurs in the comparison phase
Because no print is ever perfectly replicated, mental comparative assessment consider tolerance for variations in appearance caused by distortion
Makes comparative measurements of first, second, and third level details are made along with comparisons of the sequences and configuration of ridge pathsSlide24
Examination Method
Evaluation
The formulation of a conclusion base upon analysis and comparison of friction ridge skin
The examiner makes the final determination as to whether a finding of individuation or same source of origin can be made Makes comparative measurements of first, second, and third level details are made along with comparisons of the sequences and configuration of ridge pathsSlide25
Examination Method
Recurring, Reversing, and Blending Application of ACE
The examiner can change the phase of the examination often re-analysis, re-compares, and re-evaluates.
There is no clear linear path to this ACE process because the decision of choosing whether the two fingerprints are the same complicates things.Slide26
Examination Method
Because of the ambiguity of the process the colored diagram is used to illustrate the process.
The critical application of ACE is represented in the model by red area A, green area C and blue area E
The actual examination is represented in the model by
threee
smaller circles with capital A, C, and E. Slide27
Examination Method
The black dot in the center represents the subconscious processing of detail in which perception can occur
The gray represents other expert knowledge, beliefs, biases, influences and abilities.
The white that encircles the grey represents the decision has be made
Many evaluation take place. Eventually the final analysis and comparison lead to the final evaluation Slide28
Examination Method
Verification
The independent examination by another qualified examiner resulting in the same conclusion
It is another person going through the ACE process of verifying if the two prints conclusion are the same The verifier must not know the decision of the previous conclusion to get decisions that is nonbiasedSlide29
Decision Thresholds
Decisions must be made within each phase of ACE whether to go foreword, backwards, or to stop in the examination process must be decided
History of threshold:
New Scotland Yard adopted a policy (with some exceptions) of requiring 16 pointsThe FBI abandoned the practice of requiring a set number of points
The IAI (International Association for Identification) formed a committee to determine the minimum number of friction ridge characteristics which must be present in two impressions in order to establish positive identification Slide30
Decision Thresholds
The prevailing threshold of sufficiency is the examiners determination that sufficient quantity and quality of detail exists in the prints being compared
Quantitative-qualitative threshold (QQ)
For impressions from volar skin, as the quality of details in the prints in creases, the requirement for quantity of detail in the prints decreases, as the quantity of details decrease
For clearer prints, fewer details are needed and for less clear prints, more details are neededSlide31
QQ Threshold Curve
One unit of uniqueness in agreement is the theoretical minimum needed to determine the prints had been made by the same unique and persistent sourceSlide32
QQ Threshold Curve
Agreement (white area): sufficient detail agree and support a determination that the prints came from the same source
Disagreement (white area) sufficient details disagree and warrant a determination that the prints came from different sources
Inconclusive (gray and black areas): the examiner cannot determine whether the details actually agree or disagree or cannot determine sufficiency of sequences and configurations Slide33
APPLICATION OF SPATIAL STATISTICS TO LATENT PRINT IDENTIFICATIONS Slide34
Methodology
•
Ten
-print cards
- Qualitative image
assessment
•Scan, segregate and image enhancement
•Orientation, ULW minutiae detection, mark core and delta
•Geo-referencing and image QC
•GIS data conversion
•Spatial analysis of ridge lines and minutiae
•Statistical analyses and probability modeling Slide35Slide36
Extraction Software
Free Fingerprint Imaging Software
-- fingerprint pattern classification, minutiae detection, Wavelet Scalar Quantization(
wsq) compression, ANSI/NIST-ITL 1-2000 reference implementation, baseline and lossless jpeg, image utilities, math and neural net libs
Universal Latent Workstation (
ULW
)
-- interoperable and interactive software for latent print examiners. The software improves the exchange and search of latent friction ridge images involving various Automated Fingerprint Identification Systems.Slide37Slide38Slide39
Distribution of MinutiaeSlide40Slide41Slide42Slide43Slide44Slide45
Geometric Morphometric Analysis
Research on fingerprints traditionally done using biometrics, which analyze linear geometric properties but ignore underlying biological properties
Ignoring these may exclude important bio patterns
Biomathematics include inherent biological properties of featuresGM is a biomathematical
model that includes biometrics, along with other fields for a comprehensive analysisSlide46
GM Analysis
Used for mandibular morphology, craniofacial features, identification using sinus cavities, pediatric skeletal age
For this project, GM used to study shape variation of four pattern types: left and right loops, whorls, and double loop whorls
GIS used for efficiencyTasks: Establish Methodology. Begin Analysis.Slide47
Method: Landmark and Semi-landmark Designation and Acquisition
30 images each referenced with
arcGIS
to find core and align in coordinate spaceLandmarks – Core, aspects of the deltaSemi-landmarks – Points along a ridgeline
For loops the core was defined as the point along the innermost ridgeline that forms the first full loop where the tangential angle is closest to 0 degrees
For whorls and double loop whorls, core defined as ridge ending in the middleSlide48
Method: Landmark and Semi-landmark Designation and Acquisition
Delta defined as a
triradius
consisting of 3 ridge systems converging with each other at an angle ~ 120 degreesA equilateral triangle, sized as small as possible, placed manually to define the delta. 100% consensus among team requiredSlide49
Method: Landmark and Semi-landmark Designation and Acquisition
Core and vertices of triangles defined as landmarks
For loops:
Radial line template of seven lines, eighteen degrees apart. Intersections of lines and first continuous ridgeline are semi-landmarksSlide50
For loops:
Two reference lines, one vertical, going through core; one horizontal from lowermost vertex to vertical line
Ten equidistant lines drawn from core to horizontal line
Where top six lines intersect with ridgeline that the core is on are more landmarks
Method: Landmark and Semi-landmark Designation and AcquisitionSlide51
For whorls:
Line template constructed with thirteen lines, nine degrees apart
Intersection of lines with first continuous ridgeline were landmarks
After defining landmarks and semi-landmarks, GIS used to record the features for all 120 prints
Method: Landmark and Semi-landmark Designation and AcquisitionSlide52
Method: Generalized
Procrusted
Analysis
Landmark and semi-landmark coordinates superimposed into a coordinate system in order to conduct statistical analysisCalculated Procrustes mean shape valuesSlide53
Method: Generalized Procrusted
Analysis
RSL and LSL, W and DLW superimposed onto each other with geometric transformations to determine varianceSlide54
Method: Thin-Plate Spline
Procrustes
mean shape values analyzed using R statistical software to produce TPS deformation gridsSlide55
Method: Thin-Plate Spline
TPS grids provide a smooth interpolation of inter-landmark space and provide exact mapping for landmarks and semi-landmarks from one pattern type onto anotherSlide56
Method: Principle Component Analysis
Captured a percentage of total variation based on distribution to summarize original larger data set
Direction of relative displacement for each landmark determinedSlide57
Results: Generalized Procrustes
Analysis
LSL: semi-landmarks were tightly clustered around mean shape showing little shape variation for both core ridgeline and continuous ridgeline. Large dispersion of delta landmarks and crease landmark
Whorls: Continuous ridgeline showed little shape variation. Delta and crease landmarks showed significant variation
LSL-RSL: greater dispersion due to size variation and rotational effects
W-DLW: same as LSL-RSLSlide58
Results: Thin-Plate Spline
The greater the deformation in the grid, the more shape variation between the two
RSL-LSL: high degree of shape consistency with greatest variation in the delta region
W-DLW: same as RSL-LSLSlide59
Results: Principle component analysis
Calculations used to reduce total of landmarks and semi-landmarks to one set to summarize degree of shape variation in each pattern type
Direction of variation represented by vector line
Degree of variation indicated by amount of deformation in gridRSL-LSL: different directions of variation, greatest variation in delta regions
W-DLW: greatest variation in delta regionsSlide60Slide61
False-Match Probabilites
and Monte Carlo Analyses
“A computer algorithm used to repeatedly
resmaple data from a given population to make inferences about stochastic processes”Ideal for rare events, hard to analyze rare events with other methods
Goal is to produce an expected result, E(X) where X is a random variable. MC
sim
creates n independent samples of X, and as n increases, the average of the samples converges to the expected resultSlide62
False-Match Probabilites
and Monte Carlo Analyses
Used for village placement to avoid natural disasters, species diversity, evolution, air traffic control
For this project: There is biological ground to believe that fingerprints are unique, but statistics allows for duplicatesUniqueness not in question, but partial uniqueness is possible. Since examined prints are rarely full, need to see chances of partial duplicatesSlide63
Methods and background are numerically and theoretically intensive, so will email paper to those more interested
No assumptions – works well for small sample sizes, but assumptions must be used for larger numbers
Compared different sample sets to determine probability of a false-match
1200 fingerprintsFalse-Match Probabilites
and Monte Carlo AnalysesSlide64
GISStandardize coordinate space and analyze print by section
Eight simulations to determine how each attribute affects false-match probabilities
Nine overlapping grid cells and total minutiae in each cell counted
Sets of three, five, seven, or nine minutiae selectedFalse-Match Probabilites
and Monte Carlo AnalysesSlide65Slide66Slide67
Minutiae selected without replacement
50 prints selected for LSL, RSL, W, DLW
20 prints selected for arches, 25 for tented arches
Simulations iterated 1000 timesComparisons across and within pattern typesNeeded to account for variance of each minutiaeBifurcation angles, ridge ending roundness, etc.
False-Match
Probabilites
and Monte Carlo AnalysesSlide68
MC Results
Similar probability results for all pattern types
As robustness of simulation expanded, probability of false match decreased greatly
Using all criteria with location, three minutiae has a false-match chance of 1 in 5 millionUsing only location, chance is 1 in 1600Using only location with 5 minutiae, chance is 1 in 125000
Only one false match found when considering position of 9 minutiaeSlide69Slide70
MC Results
Highest false match probability in regions below core and near delta (more minutiae)
Regions above core have very low false match probability (less minutiae)
Most matches found using Monte Carlo are obviously not matches when examinedSimilar patterns of minutiae, but not type were foundSmall sample size limits conclusions
100,000 fingerprints considered desirable for strong results (6-7 weeks of computer time)Slide71