By Davide Maltoni, Dario Maio, Anil K. Jain, Salil Prabhakar
This guide offers in-depth insurance of the latest advances and practices. Readers will locate complete and authoritative assurance of the entire significant strategies, issues, and defense equipment linked to fingerprint popularity. Written with an identical formulation that made the luck of the 1st version, the e-book comprises the latest state of the art ideas within the box, in addition to superior and up to date chapters overlaying sensor expertise, functionality review, criteria, and securing fingerprint platforms. The accompanying DVD additionally presents FVC2004 DB. The moment variation of this complete and authoritative reference, written by way of prime overseas researchers, covers all severe points of fingerprint protection structures and know-how. it really is a fully crucial source for all biometric safety pros, researchers, practitioners, builders, and platforms directors.
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Extra info for Handbook of fingerprint recognition
Typical operating points of different applications displayed on an ROC curve. , the entire database containing N templates is searched), that a single template for each finger is present in the database, and that all imposter matching scores are independent. Since identification error rates are dependent on the number of templates N to be searched, let us denote false negative identificationerror rate as FNIRN and false positive identification-error rate as FPIRN. 5 System Errors • • 21 FNIRN = FNMR; the probability of false negative error when searching the identification feature set against N templates is the same as the false non-match error in verification mode (except that this expression does not take into account the probability that a false match may occur before the correct template is visited, see Cappelli, Maio, and Maltoni (2000c)).
In open-set identification, some of the identification attempts are made by subjects who are not enrolled. In the rest of this book when we refer to identification we will focus only on the open-set scenario. If the matching module is given a feature set from finger A and a set of templates that includes at least one template of A, and the matching module produces an empty candidate list, the error is called a false negative identification error. If the matching module is given a feature set from finger A and a set of templates that does not include any template from A, and the matching module returns a non-empty candidate list, the error is called a false positive identification error.
There is a tradeoff between the FTE rate and the error rates of the matching module discussed below. If the failure to enroll is disabled, then templates can be created from poor quality fingerprints but such noisy templates would result in higher matching errors. 5 Matching module errors The result of a fingerprint matching module is typically a matching score (without loss of generality, lying in the interval [0,1]) that quantifies the similarity between the recognition feature set and the enrollment template.