  This work expands on previous advancements in genetic fingerprint spoofing
via the DeepMasterPrints and introduces Diversity and Novelty MasterPrints.
This system uses quality diversity evolutionary algorithms to generate
dictionaries of artificial prints with a focus on increasing coverage of users
from the dataset. The Diversity MasterPrints focus on generating solution
prints that match with users not covered by previously found prints, and the
Novelty MasterPrints explicitly search for prints with more that are farther in
user space than previous prints. Our multi-print search methodologies
outperform the singular DeepMasterPrints in both coverage and generalization
while maintaining quality of the fingerprint image output.
