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Transcript of NIST Forensic Science Activities: Latent Fingerprints · “promote the development o forensic...
NIST Forensic Science Activities:
Latent Fingerprints
2009 National Academies Report
“Strengthening Forensic Science in the United States: A Path Forward” � Accuracy, reliability, and validity of latent fingerprint analysis
� Standards for data interoperability & sharing
� Human factors research to advance
� Advanced measurement of technologies to automatically map record & recognize
validation, reliability & proficiency testing
“ t th d l t f f i
to automatically map, record & recognize latent fingerprint features
“promote the development of forensic science [as applied to latent fingerprints] into a mature field of multi-disciplinary
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research and practice”
Rolled (ink capture)
What are Latent Fingerprints?
Pl i (i
“Conventional” Fingerprints Latent Fingerprint
( lift)Plain (inkk capture)) (powdder lift)
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Tip
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Why Are Latents Difficult?
Smudge Discontinuous Ridges
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Why Are Latents Important?
Homeland Security Military & IntelligenceLaw Enforcement / Criminal Justice
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NIST Fingerprint Milestones
� 1960s
� 1986
� 2000
� 2003
� 2006
� 2009
� 2010
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– NIST reports feasibilityy of automated finggerprint matchingg
– 1st standard for fingerprint exchange: ANSI/NIST
– ANSI/NIST widely used in the US and internationally
– 1st largge-scale automatic fingerprint matching test ((FppVTE))g p g • DHS uses results for developing operational systems
– NIST initiates testing of latent fingerprint matching systems
– NIST tests use of extended features sets for latent ID • FBI uses results for Next Generation Identification system
– ANSI/NIST adopts extended feature sets
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e e e ce data
Customers and Community
Industryy
Government
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� Developers & Researchers: • Measurement & failure analysis • Standards • Conferences, Journals • Reference data Academia
� Users & Practitioners: • Performance rankings • System integration & tuning • Standards
Law Enforcement• Conferences,, Workshopps
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Framework: Latent ID Search
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS) Latent Fingerprint & Search Features
Input Output
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Comparison Features
Input Output 1 0733091
2 13040332
3 3952340
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Examiner & Workstation Examiner & Workstation
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Latent Workstation
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS) Latent Fingerprint & Search Features
Input Output
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Comparison Features
Input Output 1 0733091
2 13040332
3 3952340
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Latent Workstation: Feature Markup
Ridge ending
Examiner & Workstation
Core
Examiner & Workstation
Core
Core Indeterminate
Bifurcation
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Latent Workstation Screenshot
Usability Research
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� Design tools and human-computer interfacescomputer interfaces that arethat are efficient, effective, and intuitive
� New interaction paradigms: moving away from mouse-and-click to touch technologies that mimic the wayy examiners work with photographs, loupes, and pins
� Elimination of examiner bias through standardization of processes and procedures
�� P lidi li bili dPromote validity, reliability, and traceability
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Interoperable Feature Standards
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS) Latent Fingerprint & Search Features
Input Output
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Comparison Features
Input Output 1 0733091
2 13040332
3 3952340
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The ANSI/NIST-ITL Standard
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Legacy of Evolving Interoperability:
� 1986 – Standardized Minutiae
� 1993 – Standardized Images
� 2000 – Proprietary Feature Blocks
� 2010 – Extended Features
� Soon – Universal Standard Features for Interoperable Search
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Feature “Evolution” Standard Feature Markup Extended Feature Markup
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Extended Feature Set (EFS) � Feature quality map
• interoperable � Complete feature set:
Ridge ending
� Complete feature set: • pores • ridge shape • incipient ridges
Incipient
• dots • creases • scars • ridge pathsp
Protrusion
g p
� Improved definitions � Correspondences
Core
Pore Core
Dot
Indeterminate Bifurcation
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Latent ID Search Performance
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS) Latent Fingerprint & Search Features
Input Output
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Comparison Features
Input Output 1 0733091
2 13040332
3 3952340
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100
%) 80
90
60
70
40
50
Iden
n
Rat
e (%
ntifi
catio
n
20
30
0
10
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Plain Search
Latent Search
State-of-the-art Performance Measurement:
Plain vs Latent Print Search Accuracy Plain vs. Latent Print Search Accuracy
37%37%
Fingerprint Type Fingerprint Type
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“Lights out” Search
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS) Latent Fingerprint & Search Features
Input Output
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Comparison Features
Input Output 1 0733091
2 13040332
3 3952340
Extract
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State-of-the-art Performance Measurement
“Lights out” vs Latent + manual mark-up
Image Only Image + Feature Markup
Lights out vs. Latent + manual mark up ) 80
90
100
Rat
e (%
)
60
70
80
tific
atio
n
40
50
Iden
t
10
20
30
0
MorphoTrack NEC Cogent Warwick
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Lights Out Lights Out
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Latent Image Quality: “Triage”
? Examiner ? Examiner
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Candidate List Review
List of Candidates L t t Fi i t &
Automated Latent Identification System
(AFIS)
Rank Subject ID
Candidate Print
1 0733091
Latent Fingerprint & Search Features
Input Output
Latent Fingerprint & Comparison Features
1 0733091
2 13040332
3 3952340
Input Output
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Examiner & Workstation Examiner & Workstation
Match
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Future Work
� Measurement of large-scale latent ID search accuracy, and the time vs. accuracy trade-off: • prediction of large-scale operational system performance • better understandingg of resource reqquirements
� Evaluation of interoperable latent feature sets for ID search • interoperability at a higher level of accuracy
� Measurement and Calibration of Latent Quality algorithms & metrics • workflow optimization schemes (quality directed processing) • less manual processing Î decreased case backlog less manual processing Î decreased case backlog
� Evaluation/Validation of match probability models • statistically quantifiable examiner match decisions
� Evaluation of automated latent palm-print matching systems
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Questions?
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Match Probability Models
MATCH99 MATCH
NON-MATCH 99
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+ % Confidence
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