Fingerprint Synthesis Using Deep Generative Models
The advancements in biometric technology have amplified the need for more robust fingerprint synthesis techniques. In this thesis, we first explored the application of synthesizing normal fingerprint images in high fidelity using deep generative models (e.g., generative adversarial networks and diffusion models) and created synthetic fingerprints that retain the uniqueness and complexity of the or
