Download - Quality Metrics for Pattern Evidence
University of Virginia, Charlottesville VA 22904
This work was partially funded by the Center for Statistics and Applications in Forensic Evidence (CSAFE) through Cooperative Agreement #70NANB15H176 between NIST and Iowa State University, which includes activities carried out at Carnegie Mellon University, University of California Irvine, and University of Virginia.
Project Rationale & Goals Results & Discussion
Materials & Methods
Conclusions Acknowledgements
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Quality Metrics for Pattern EvidenceKaren Pan, Karen Kafadar
Project Rationale & Goals Results & Discussion
Materials & Methods
Conclusions Acknowledgements
Given a latent fingerprint, can we use print quality to determine the probability LPEs will find the right match?
• Develop objective measure of quality, correlate with accuracy
• Estimate probability LPEs makecorrect ID or exclusion
• Focus elsewhere if QM < threshold
Analysis of entire process, from quality score calculation to final assessment after ACE-V
Latent Print Examiners (LPEs)
• Need examined prints of known quality not to evaluate LPEs, but only to provide data on relationship between print quality and accuracy
Fingerprint Databases
• NIST SD27a pairs not necessarily ground truth
• Creation of database (Professor Keith Inman, California State East Bay)
• Houston Forensic Science Center (HFSC)
• Blind verification latents, LPEs
• Challenges: replication on a single print (physical card)
Global quality scores for three NIST SD27a latents CTS proficiency test latent print images
Good (G008) Bad (B106) Ugly (U2335)
Contrast Gradient Algorithm provides feature scores
• Objective assessment of quality and empirical measure of accuracy for varying quality levels
• Objective assessment of expected performance
• Include other QMs as available (NIST, MSU, etc.)
• Other pattern evidence (ballistics, tool marks, tire treads, shoe prints) where evidence comes as images
• Objective assessment of “level of difficulty” in proficiency tests and experiments comparing different approaches
• Assessment of entire fingerprint comparison process
• CSAFE (NIST), UVA, Isaac Newton Institute
• A. Peskin (NIST), K. Inman (CSU-EB), H. Swofford, A. Rairden (HFSC), S. Huckeman (Gottingen), R. A. Hicklin (Noblis), B. Gardner (UVA), HFSC
Quality Metric (QM) Score
Type
Score
Range
Requires
features
Description
Contast Gradient
(Peskin and Kafadar)
Feature 0-100 Y Examines gradient of contrast
intensity around a feature
DFIQI
(Swofford)
Feature 0-100 Preferred Combination of 5 aspects (e.g., ridge
width, acutance (sharpness),
contrast, etc.)
Latent Quality Metrics
(LQM)
Global 0-100 N Score indicates predicted probability
an image only search returns the
mate; *VID and VCMP; and 9
metrics calculated from a latent
SNoQE
(Richter et al. 2019)
Global 0-1 N (ROI if
possible)
Wavelet-based measure of amount
of smudge in image
* VID (value for individualization) – a latent is VID if an examiner would assess it to have sufficient quality for individualization; VCMP (value for comparison) – print is of sufficient quality for individualization or exclusion
Print LQM SNoQE
Good 71 0.7549
Bad 41 0.7930
Ugly 15 0.6165
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2
3 4
5
Feature X Y Score
1 53 13 29.4628
2 48 23 33.7415
3 14 66 79.8615
4 73 70 31.6978
5 48 128 23.7921
LQM VID, VCMP SNoQE
1 88 100, 100 0. 9693
2 72 98, 99 0. 9438
3 69 98, 99 0. 9123
4 60 96, 99 0. 9148
5 99 100, 100 0. 9607
6 72 98, 99 0. 9798
7 77 98, 100 0. 9144
8 87 100, 100 0. 9576
9 78 99, 100 0. 8526
10 96 100, 100 0. 9647
11 67 97, 99 0. 8679
1 2 4
5
6 7 8
9 10 11
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