{"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/automatic-latent-fingerprint-segmentation","title":"Automatic Latent Fingerprint Segmentation","arxiv_id":"1804.09650","date":"2018-04-25","proceeding":null,"authors":["Dinh-Luan Nguyen","Kai Cao","Anil K. Jain"],"abstract":"We present a simple but effective method for automatic latent fingerprint\nsegmentation, called SegFinNet. SegFinNet takes a latent image as an input and\noutputs a binary mask highlighting the friction ridge pattern. Our algorithm\ncombines fully convolutional neural network and detection-based approaches to\nprocess the entire input latent image in one shot instead of using latent\npatches. Experimental results on three different latent databases (i.e. NIST\nSD27, WVU, and an operational forensic database) show that SegFinNet\noutperforms both human markup for latents and the state-of-the-art latent\nsegmentation algorithms. We further show that this improved cropping boosts the\nhit rate of a latent fingerprint matcher.","url_abs":"http://arxiv.org/abs/1804.09650v2","url_pdf":"http://arxiv.org/pdf/1804.09650v2.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":"automatic-latent-fingerprint-segmentation","repo_url":"https://github.com/luannd/MSU-LatentAFIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"automatic-latent-fingerprint-segmentation","repo_url":"https://github.com/luannd/MinutiaeNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"automatic-latent-fingerprint-segmentation","repo_url":"https://github.com/prip-lab/MSU-LatentAFIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"friction","task_name":"Friction"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}