  Fingerprint verification and identification algorithms based on minutiae
features are used in many biometric systems today (e.g., governmental e-ID
programs, border control, AFIS, personal authentication for portable devices).
Researchers in industry/academia are now able to utilize many publicly
available fingerprint databases (e.g., Fingerprint Verification Competition
(FVC) & NIST databases) to compare/evaluate their feature extraction and/or
matching algorithm performances against those of others. The results from these
evaluations are typically utilized by decision makers responsible for
implementing the cited biometric systems, in selecting/tuning specific sensors,
feature extractors and matchers. In this study, for a subset of the cited
public fingerprint databases, we report fingerprint minutiae matching results,
which are based on (i) minutiae extracted automatically from fingerprint
images, and (ii) minutiae extracted manually by human subjects. By doing so, we
are able to (i) quantitatively judge the performance differences between these
two cases, (ii) elaborate on performance upper bounds of minutiae matching,
utilizing what can be termed as "ground truth" minutiae features, (iii) analyze
minutiae matching performance, without coupling it with the minutiae extraction
performance beforehand. Further, as we will freely distribute the minutiae
templates, originating from this manual labeling study, in a standard minutiae
template exchange format (ISO 19794-2), we believe that other researchers in
the biometrics community will be able to utilize the associated results &
templates to create their own evaluations pertaining to their fingerprint
minutiae extractors/matchers.
