Fingerprint Generation

Fingerprint generation research focuses on creating synthetic fingerprints for various applications, primarily to address privacy concerns related to using real biometric data and to improve the performance of fingerprint recognition systems. Current research heavily utilizes generative adversarial networks (GANs) and diffusion models, often incorporating multimodal conditioning to control generated fingerprint characteristics like type, quality, and sensor type, thereby enhancing realism and utility. This work is significant because synthetic fingerprints can augment training datasets for improved recognition accuracy, enable the development of more robust fingerprint-based security systems, and facilitate research without compromising sensitive personal information.