Face Identification is Difficult - NNA library/nna/conference/2016/using... · Using Proven...
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1 Using Proven Facial-Recognition Practices to Identify Signers Dr. Megan Papesh Louisiana State University Face Identification is Difficult
Transcript of Face Identification is Difficult - NNA library/nna/conference/2016/using... · Using Proven...
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Using Proven Facial-Recognition
Practices to Identify Signers
Dr. Megan Papesh
Louisiana State University
Face Identification is Difficult
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What Do We Know?
Expertise doesn’t seem to exist
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Look-alike
Cardholder Similarity
% “Stolen” IDs Accepted
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Look-alike Very different
Cardholder Similarity
% “Stolen” IDs Accepted
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Students Passport Officers
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orr
ect
Matching IDs
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Students Passport Officers
% C
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ect
Mismatching IDs
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Students Bank Tellers Notaries
Correct IDs Matched
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Students Bank Tellers Notaries
“Stolen” IDs Spotted
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Years of Professional Notary Experience
Matching Correct IDs R² = 0.0005
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Years of Professional Notary Experience
Matching Correct IDs
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Years of Professional Notary Experience
Spotting “Stolen” IDs R² = 0.0045
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Spotting “Stolen” IDs
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Weekly ID Verifications
Matching Correct IDs R² = 0.0014
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Weekly ID Verifications
Matching Correct IDs
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Weekly ID Verifications
Spotting “Stolen” IDs R² = 0.0036
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Weekly ID Verifications
Spotting “Stolen” IDs
What Can We Do About This?
Errors come from two sources:
Perceptual failures
Cognitive failures
Perceptual Failures
Identification involves
detection of both
similarities and
dissimilarities
It also involves detecting,
and discounting,
explainable discordances
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Expert Examiners Use ACE-V
Analysis
Objective scanning, subjective assessment
Comparison
Note similarities, dissimilarities, discordances
Evaluation
Explanations for dissimilarities/discordances?
Verification
Analysis
Processing faces in this way does not come naturally.
Comparison
Your job is tougher!
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Comparison
Comparison
Comparison
Teal beats Purple!
What did purple do wrong?
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Evaluation
When you note differences, they can be…
Explainable
Unexplainable
Exclusionary
Practice Evaluation
Explainable Differences?
Unexplainable Differences?
Exclusionary Differences?
Explainable Differences?
Unexplainable Differences?
Exclusionary Differences?
Practice Evaluation
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Explainable Differences?
Unexplainable Differences?
Exclusionary Differences?
Practice Evaluation
Practice Evaluation
Practice Evaluation
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Practice Evaluation
Verification
Make a confident decision
Ask to see another photo?
Research shows that more photos = better
performance
The ACE-V Method
Analysis
Try not to think of it as a face. Faces are special.
Comparison
What are the similarities and dissimilarities?
Evaluation
Can you explain the dissimilarities?
Verification
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Cognitive Failures
Cognitive Failures
Cognitive biases fall into two types:
Change blindness
Getting into a rut
Change Blindness
Or… “you see what you expect to see”
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How many shoppers
noticed the switch?
Grapefruit Cinnamon-apple
Cinnamon-apple Grapefruit A) 14%
B) 28%
C) 42%
D) 56%
Change Blindness
Expectations are powerful
If you don’t expect someone to
present a false or stolen ID, then you
won’t see it!
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Getting Into a Rut
Or… “low-prevalence effects”
It becomes harder to
“see” things that you
don’t see very often.
Getting Into a Rut
Stronger effects in face identification
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Mis
s R
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(%)
Target Frequency
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Mis
s R
ate
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Target Frequency
First Decision Second Decision
Participants “missed” fake IDs more when they were
uncommon.
Even when given a “second chance,” they still failed to
spot rare fake IDs.
Getting Into a Rut
Like change blindness, expectations are
based on experience
Only alleviated by changing expectations
Think of it as a hazard function of probability
Start to “expect” the false/stolen ID
Best Practices
ACE-V
Ignore mutable features (hair, in particular)
Focus on stable features (earlobes, relationships between features)
Change your expectations
With each valid ID, raise your suspicions of the next one
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Back to the Beginning
Thank You
The National Notary Association, especially…
Michael Lewis, Bill Anderson, Gerardo Rodriguez, Brooke Murphy
All of the 1000+ members who participated in the online survey!
University Collaborators, especially…
Steve Goldinger, Laura Heisick, Caroline Rausch, Juan D.
Guevara-Pinto
Funding Sponsors
NIH NIDCD R01-DC04535-08-13
Citations
Slide 4: Kemp et al. (1997)
Slide 5: White et al. (2014)
Slides 6 – 10: Papesh (in prep)
Slide 12: White et al. (2015)
Slides on ACE-V: Proceedings of the FISWG (www.fiswg.org)
Slide 26: Bindemann & Sandford (2011)
Slide 30: Simons & Levin (1998)
Slide 31: Hall et al. (2010)
Slide 34: Wolfe et al. (2005)
Slide 36: Papesh & Goldinger (2014)
