{"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/automated-latent-fingerprint-recognition","title":"Automated Latent Fingerprint Recognition","arxiv_id":"1704.01925","date":"2017-04-06","proceeding":null,"authors":["Kai Cao","Anil K. Jain"],"abstract":"Latent fingerprints are one of the most important and widely used evidence in\nlaw enforcement and forensic agencies worldwide. Yet, NIST evaluations show\nthat the performance of state-of-the-art latent recognition systems is far from\nsatisfactory. An automated latent fingerprint recognition system with high\naccuracy is essential to compare latents found at crime scenes to a large\ncollection of reference prints to generate a candidate list of possible mates.\nIn this paper, we propose an automated latent fingerprint recognition algorithm\nthat utilizes Convolutional Neural Networks (ConvNets) for ridge flow\nestimation and minutiae descriptor extraction, and extract complementary\ntemplates (two minutiae templates and one texture template) to represent the\nlatent. The comparison scores between the latent and a reference print based on\nthe three templates are fused to retrieve a short candidate list from the\nreference database. Experimental results show that the rank-1 identification\naccuracies (query latent is matched with its true mate in the reference\ndatabase) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases,\nagainst a reference database of 100K rolled prints. These results are the best\namong published papers on latent recognition and competitive with the\nperformance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB,\nrespectively) of a leading COTS latent Automated Fingerprint Identification\nSystem (AFIS). By score-level (rank-level) fusion of our system with the\ncommercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification\nperformance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6%\n(78.4%) on NIST SD27 and WVU latent databases, respectively.","url_abs":"http://arxiv.org/abs/1704.01925v1","url_pdf":"http://arxiv.org/pdf/1704.01925v1.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":"automated-latent-fingerprint-recognition","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":"automated-latent-fingerprint-recognition","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":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}