2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | IFS-1.10 | ||
| Paper Title | HIGH FIDELITY FINGERPRINT GENERATION: QUALITY, UNIQUENESS, AND PRIVACY | ||
| Authors | Keivan Bahmani, Richard Plesh, Peter Johnson, Clarkson University, United States; Timothy Swyka, Precise Biometrics, United States; Stephanie Schuckers, Clarkson University, United States | ||
| Session | IFS-1: Biometrics | ||
| Location | Area K | ||
| Session Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation | Poster | ||
| Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Biometric Analysis | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, 512 x 512 pixel, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/Clarkson_Finger_Gen | ||