{"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/fpd-m-net-fingerprint-image-denoising-and","title":"FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks","arxiv_id":"1812.10191","date":"2018-12-26","proceeding":null,"authors":["Sukesh Adiga V","Jayanthi Sivaswamy"],"abstract":"Fingerprint is a common biometric used for authentication and verification of\nan individual. These images are degraded when fingers are wet, dirty, dry or\nwounded and due to the failure of the sensors, etc. The extraction of the\nfingerprint from a degraded image requires denoising and inpainting. We propose\nto address these problems with an end-to-end trainable Convolutional Neural\nNetwork based architecture called FPD-M-net, by posing the fingerprint\ndenoising and inpainting problem as a segmentation (foreground) task. Our\narchitecture is based on the M-net with a change: structure similarity loss\nfunction, used for better extraction of the fingerprint from the noisy\nbackground. Our method outperforms the baseline method and achieves an overall\n3rd rank in the Chalearn LAP Inpainting Competition Track 3 - Fingerprint\nDenoising and Inpainting, ECCV 2018","url_abs":"http://arxiv.org/abs/1812.10191v2","url_pdf":"http://arxiv.org/pdf/1812.10191v2.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":"fpd-m-net-fingerprint-image-denoising-and","repo_url":"https://github.com/adigasu/FDPMNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"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}