quality metrics for pattern evidence
TRANSCRIPT
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
1
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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