Advances in Fingerprint Technology.pdf

the expected number of matches required to solve the identification problem
increases linearly with the database size. It is therefore desirable to find more
efficient solutions to the identification problem.
There have been two primary approaches to make the identification
searches more efficient. In the first approach, the database is organized so that
certain matches can be ruled out based on the information extrinsic/intrinsic
to the fingerprints. When the number of necessary matches is reduced based
on the information extrinsic to the fingerprints, the solution is commonly
referred to as filtering. For example, the database can be presegmented based
on information about sex, race, age, and other bio-/geographical information
related to the individual. In binning, a fingerprint’s intrinsic information
(e.g., fingerprint class) is used to reduce the number of matches. 61
The percentage of the total database to be scanned, on average, for each
search is called the “penetration coefficient,” P, which can be defined as the
ratio of the expected number of comparisons required for a single input
image to the total number of prints in the entire database. Based on published
results, we believe that binning can achieve a penetration coefficient of about
50%. The second approach for making the search more efficient is to reduce
the effective time given for each match. Given a matching algorithm, the
effective time per match can be reduced by directly implementing the entire
algorithm or components of it in special hardware. The other method of
reducing the effective time per match is by parallelizing the matches, that is,
using multiple processors and assigning a fraction of the matches to each
processor. Some vendors have resorted to optical computing 41 to achieve a
very high matching throughput.
Scalability of accuracy performance of a large-scale identification system
is a more formidable challenge than its speed performance. If the accuracy
performance associated with matching each pair of fingerprints (e.g., verification
accuracy) is characterized by false accept (FAR v) and false reject (FRR v)
rates, the identification accuracy performance of the system with n records
in the database (one per identity) can then be expressed as:
FRRi = FRRv × FCR
n P
i ( v)
×
FAR = 1− 1−FAR
(8.3)
(8.4)
under the underlying assumptions that (1) the outcome of each match is an
independent event, (2) all the records in the database are correctly classified,
(3) FCR is the probability of falsely classifying (binning) the given fingerprint
into a wrong bin, and (4) the misclassification and mismatching events are
independent.