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Identification System Errors

Guide to Biometrics – Chapter 6 Handbook of Fingerprint Recognition - 1.4 Presented By: Chris Miles. Identification System Errors. Extending to Identification. How do we extend our numerical models for verification errors for identificatation? FNMR – False Non Match Rate

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Identification System Errors

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  1. Guide to Biometrics – Chapter 6 Handbook of Fingerprint Recognition - 1.4 Presented By: Chris Miles Identification System Errors

  2. Extending to Identification • How do we extend our numerical models for verification errors for identificatation? • FNMR – False Non Match Rate • FMR – False Match Rate • What Issues are presented

  3. Identification System • Maintains a database of enrolled users • Tries to match input against the database • Positive Identification == Negative Identification • Output • List of best matches – Ideally just the true identity • Best Match • yes/no in the list

  4. Example • Casino using face detection to identify people on the Nevada Gaming Commission's black list • http://gaming.nv.gov/loep_main.htm • Basis for other government biometrics systems • N = the number of people on the list • M = number of people through the casino daily • Calculate FNMRN and FMRN

  5. Matching System • Parallel version of your favorite verification algorithm • Attempt to match all users against the database

  6. FNMRN • The chance of being falsely rejected is the same as verification • Chance of not matching against your template – chance of matching someone else's template • Assuming no FMR, FNMRN = FNMR

  7. FMRN • FMRN = Chance of matching someones template ^ number of templates • FMRN = 1 – (1 – FMR)N • Number of daily false matches = M * FMRN= M (1 - (1-FMR)N)

  8. Accuracy Scales Worse then Computation • The chance of being falsely accepted rises exponentially with the number of templates • Suppose algorithm is 99.99% accurate • 100 people in the database • Each has 8 templates • 10,000 people through the casino a day • FMRN = 1 - .9999800 = 0.076 • FMRN * 10000 = 768 False accepts a day

  9. Winnowing • True identification is exponentially hard, so generally we compromise and just return a list of probable matches. • Input -> System -> List of Candidate Matches • A second system, biometric or a human supervisor, then tries to identify the user from the new List / Database of candidates • Candidates -> Second System -> Identity • “Passing the buck” so to speak

  10. Who's on the list? • Threshold • Apply a threshold to the similarity metric • similarity > threshold -> On the list • Rank • Take the K most similar templates • Hybrid • Take the K most similar templates so long as there similarity > threshold

  11. Weaknesses • Threshold • If several users kind of match the input, but not quite, a threshold based system would return nothing • Rank • Impostor -> List of bad matches • Solution: Generic Impostor Model -> Additional Template representing a non-match situation, if a user matches this -> returns nothing. • Hybrid • Strengths of both techniques cover the weaknesses

  12. Hybridization Ideas • Adjust K based upon how many are above the threshold • Adjust the threshold based upon the distribution of similarities

  13. Multiple Templates • Example had multiple templates per individual • Input might match mutiple templates from one person • Only one might need to be in the list • Domain Dependent

  14. Characterizing Identification • FNMR and FMR ~= Reliability and Selectivity • Reliability • 1 - FRR • How often we correctly identify someone who is in the database • Selectivity • K – Rel or • (m-1) FAR • Number of incorrect matches returned

  15. RSC, ROC, RPC Curves • These curves show the compromises involved • ROC Compromises between FAR and FRR rate • Should the vending machine take my ripped dollar and someone elses forgery? • RPC Curves • If google returned more results it would be less likely to miss relavant ones • Would include more irrelevant results however • RSC Curves

  16. Three systems • Theshold Based – Previous Example • Rank-Based identification • Rank-order statistics • Rank Probability Mass Function

  17. Threshold System Errors • Similar to previous example only returns a list of individuals above the threshold • Errors • FARM = m * FAR * (1-FAR)m-1 -Falsely Match one individual • Ambiguous answer -> List has length > 1 • P(Ambiguous) = 1 – [1 – (m+1) * FAR](1 - FAR)m-1 • FRRM = 1 - (1 – FRR) * (1 – FAR) m-1 ~= FRR

  18. Rank Based System Errors • Only works in very restricted close world scenarios (No Impostors) • Only one error – Misidentification by the correct user being ranked below another • Analyze probabilistic distribution of ranks – Rank Probability Mass Function

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