{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-end-to-end-fingerprint-denoising-and","title":"Deep End-to-end Fingerprint Denoising and Inpainting","arxiv_id":"1807.11888","date":"2018-07-31","proceeding":null,"authors":["Youness Mansar"],"abstract":"This work describes our winning solution for the Chalearn LAP In-painting\nCompetition Track 3 - Fingerprint Denoising and In-painting. The objective of\nthis competition is to reduce noise, remove the background pattern and replace\nmissing parts of fingerprint images in order to simplify the verification made\nby humans or third-party software. In this paper, we use a U-Net like CNN model\nthat performs all those steps end-to-end after being trained on the competition\ndata in a fully supervised way. This architecture and training procedure\nachieved the best results on all three metrics of the competition.","url_abs":"http://arxiv.org/abs/1807.11888v3","url_pdf":"http://arxiv.org/pdf/1807.11888v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-end-to-end-fingerprint-denoising-and","repo_url":"https://github.com/CVxTz/fingerprint_denoising","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}