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Unformatted text preview: October 4, 2005 WVU 1 Performance Evaluation BIOM 426 Instructor: Natalia Schmid October 4, 2005 WVU 2 Statistical Measures for Biometrics s False Accept Rate (FAR) FAR is the probability that a user making a false claim about his/her identity will be verified as that false identity. • Major reasons: threshold, biometrics of claimed identity is “close” to biometrics of t he user making claim. • FAR characterizes the strength of the matching algorithm. s False Reject Rate (FRR) FRR is the probability that a user making a true claim about his/her identity will be rejected as him/herself. • Major reasons: threshold, the presented biometric features are not close enough to t he biometrics template in the database. • FRR characterizes the robustness of the algorithm. s False to Enroll (FTE) FTE is the probability that a user attempting to biometrically enroll will be unable to. • Rule of 3 attempts. • Major reasons: the biometric hardware; an algorithm that is not tuned properly; a u ser who is slow in learning how to submit biometrics. s Equal Error Rate (EER) (Will be defined later) October 4, 2005 WVU 3 Basic System Errors s Before designing any system, biometric error rates have to be analyzed. s Two important facts: • Any biometric system will make a mistake; • The true value of error rates cannot be theoretically derived. s Since biometrics measurements (2D representation) or templates are available for verification of individual, a metric, S(I,T), (a measure of closeness or difference between two templates) has to be introduced. s Sources of noise: preprocessing, mapping, thermal noise, discretization, quantization, acquisition error, etc. October 4, 2005 WVU 4 Hypothesis Testing Problem s The problem of verification can be stated as a hypothesis testing problem. s Two hypothesis are possible: H0: Two samples match (null) HA: Two samples do not match (alternative) s Another statement: A and Ãare two biometrics H0: A = Ã (null) HA: A ≠ Ã (alternative) s To decide between two hypothesis, the score S(I,T) is computed H0: S(I,T) > γ (null) HA: S(I,T) < γ (alternative) October 4, 2005 WVU 5 Score Distributions s The reliability of the score is influenced by • Variations in the live biometrics in time; • Variations from sensor to sensor; • Variability in the sampling process. s Because of these reasons S(I,T) will never be one for...
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 Fall '08
 STAFF
 match, Receiver operating characteristic

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