Biometrics Individuality Dr Pushkin Kachroo Introduction Individuality View
Biometric’s Individuality Dr. Pushkin Kachroo
Introduction • Individuality: View biometric as a password • How easy is it to guess a biometric machine representation
Approaches to Individuality • Formulation of the Individuality Problem, we need – Representation of the biometric identifier – A metric of similarity of two biometric identifiers – Representation of the target population (or their representative samples)
Fingerprint Example • Fingerprint features for individuality – Location of singular points, core, delta – Ridge count between pair of minutiae – Type, direction, and location of minutiae – Location of sweat pores • However, latent prints from a crime scene might not have many features
Defining Individuality • Given a biometric sample, determine the probability of finding an arbitrary biometric sample from the target population sufficiently similar to it: Probability of False Association. • Given two fingerprints from two different fingers, determine the probability that they are sufficiently similar. • The theoretical lower bounds on False Accept and False Reject rates (intrinsic error rates) • Within-class, and between-class variations.
Calculating Individuality • Given a representation scheme and a similarity metric… • Theoretical method: model all realistic phenomenon affecting between-class and withinclass pattern variation, and then theoretically estimate probability • Empirical method: Data driven
Empirical Individuality Studies • Iris: 256 -byte binary code; use Hamming distance (http: //en. wikipedia. org/wiki/Hamming_distance) (Dougman study) 10 -52 probability of finding similar Iris patterns • Handwriting study (Srihari): False Accept Rate~5%
Theoretical Individuality Studies: A Partial Iris Model • N bit iriscode: R • N-bit query bit string: Q • Hamming distance h(Q, R) • Q and R: Real world irises • Q=Q(Q), R = R(R) are binary strings “ 0100101” of length N. • Hypotheses: – H 0: Q~R, Q and R from the same iris – Ha: Qnot~R, Q and R from different irises
FRR/FAR True bitstring Measuredbitstring
FAR Modeling Determine the probability that enough bits flip in the sensing process So that the Hamming distance is small
FAR Probability Calculations Assume i bits flip from n non-matching ones and j flip among (N-n) matching ones For False Accept: Probability i bits flip in nonmatchingn bits: Prob j bits flip in nonmatching(N-n) bits:
FAR Probability Calculations-2 Let g be the prob that individual bit agrees: (for g=1/2)
FRR Calculation For False Reject:
Fingerprint Individuality R total minutiae, K possible locations, for each location w direction Not desirable to have two minutiae at the same place…. Assume each minutiae has matching probability
Fingerprint Individuality-2 Chance of matching exactly t of the Q minuiae
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