Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning
<|reference_start|>Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning: Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancement, our method employs a specialized encoder to derive representations from fingerprint images in a self-supervised manner. We further refine these representations, aiming to enhance the verification capabilities. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance against established self-supervised training techniques. Our findings not only highlight the effectiveness of our method but also pave the way for potential advancements. Crucially, our research indicates that it is feasible to extract meaningful fingerprint representations from degraded images without relying on enhanced samples.<|reference_end|>
@article{gavas2024enhancement-driven,
    title={Enhancement-Driven Pretraining for Robust Fingerprint Representation
  Learning},
    author={Ekta Gavas, Kaustubh Olpadkar, Anoop Namboodiri},
    journal={Proceedings of the 19th International Joint Conference on Computer
  Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP
  2024) - Volume 2: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages
  821-828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10847},
    primaryClass={cs.CV}
}