Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage
This work addresses fingerprint spoofing for security systems, but it is incremental as it builds on existing DeepMasterPrints methods.
The paper tackles the problem of generating multiple artificial fingerprints to spoof more users by introducing Diversity and Novelty MasterPrints, which use quality diversity evolutionary algorithms to increase user coverage and outperform singular DeepMasterPrints in coverage and generalization while maintaining image quality.
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.