This document discusses face recognition technology and biometrics. It summarizes the results of tests on face recognition systems, distinguishing between verification, watchlist, and identification tasks. Key metrics for each task are defined such as false accept rate, detection rate, and rank-n identification. The document urges using standardized tests and proper terminology to evaluate biometrics systems.
2. Face Recognition Debate
Fall 2001
Could the hijackers have been spotted?
Biometrics Technology
3. Face Recognition Debate
Fall 2001
Can it track me?
Gregory Tara Kaye
Nancy DOB: 4/21/74
Dorsey Leflore
Dorothy Divorced Mother of 2
Shedd 2002 Income:
$45,328
Matthew
Millner
Dennis
Ricks
Joe
Grammer
Gerald
Elinor Galbraith
Pardini Allan
Olliff
Wilfred
Moesch Clayton
Tayler Weddell
Persky
Biometrics Technology Nicolas
Marocco
5. Why the confusion?
• Confusing
– Verification vs. Identification
• incorrect usage of statistics
– Identification (open set) vs. Identification
(closed set)
– False alarm rate, false accept rate, false
match rate, etc.
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6. Statistics Shortcomings
• ROC (Verification)
– Making a claim to your identity
• CMC (Identification)
– Each probe is in the database
• Are measuring closed set identification
• Neither relates to the problem!
– Solution: develop statistics that measure
this problem.
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7. Chronologically
• Face Recognition at a Chokepoint -
Scenario Evaluation Results.
http://www.dodcounterdrug.com/facialrecognition
• FRVT 2002. http://www.frvt.org.
• Both available in the Biometrics
Catalog,
http://www.biometricscatalog.org.
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9. Baseline Performance
Face Recognition Vendor
Test 2002 (FRVT 2002)
- http://www.frvt.org
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10. Three Tasks
• Verification – Are you who you say you
are?
• Watchlist – Are you in my database? If
so, who are you?
• Identification – You are in my database,
can I find you?
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11. Verification
Threshold = 0.8
Claim Response = 0.75
Threshold = 0.7
Claim Response = 0.65
Threshold = 0.6
• Determines if the claimed identity of a face is
correct.
• Metrics:
– Probability of Correct Verification
• 1- (False Reject Rate)
– False Accept
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22. Identification
• I know you are in the database, but who are you?
– Closed universe test.
• Compares your picture to all pictures in the
database and lists them according to similarity.
• Metrics:
– How often is the top answer correct?
– How often is the answer in the top 2?
– How often is the answer in the top 3?
– How often is the answer in the top n?
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24. Three Tasks
• Verification – Are you who you say you
are?
• Watchlist – Are you in my database? If
so, who are you?
• Identification – You are in my database,
can I find you?
Make sure you use the correct terms
and the correct statistics!
Biometrics Technology