  Evaluation of large-scale fingerprint search algorithms has been limited due
to lack of publicly available datasets. To address this problem, we utilize a
Generative Adversarial Network (GAN) to synthesize a fingerprint dataset
consisting of 100 million fingerprint images. In contrast to existing
fingerprint synthesis algorithms, we incorporate an identity loss which guides
the generator to synthesize fingerprints corresponding to more distinct
identities. The characteristics of our synthesized fingerprints are shown to be
more similar to real fingerprints than existing methods via eight different
metrics (minutiae count - block and template, minutiae direction - block and
template, minutiae convex hull area, minutiae spatial distribution, block
minutiae quality distribution, and NFIQ 2.0 scores). Additionally, the
synthetic fingerprints based on our approach are shown to be more distinct than
synthetic fingerprints based on published methods through search results and
imposter distribution statistics. Finally, we report for the first time in open
literature, search accuracy against a gallery of 100 million fingerprint images
(NIST SD4 Rank-1 accuracy of 89.7%).
