We present a framework for fingerprint matching based on marked point process
models. An efficient Monte Carlo algorithm is developed to calculate the
marginal likelihood ratio for the hypothesis that two observed prints originate
from the same finger against the hypothesis that they originate from different
fingers. Our model achieves good performance on an NIST-FBI fingerprint
database of 258 matched fingerprint pairs
