Fingerprint Synthesis: Search with 100 Million Prints
This addresses a data scarcity problem for researchers in biometrics and security, though it is incremental as it builds on existing GAN methods with an identity loss.
The authors tackled the lack of large-scale public datasets for fingerprint search algorithm evaluation by using a GAN to synthesize 100 million fingerprint images, achieving a NIST SD4 Rank-1 accuracy of 89.7% against this gallery.
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%).