A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations

A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 May 2025 V1 Latest version Share on A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations Authors : Jiahuai Ma 0009-0008-4623-2575 [email protected] and Alan Wilson Authors Info & Affiliations https://doi.org/10.22541/au.174733612.27353793/v1 Published Artificial Intelligence Advances Version of record Peer review timeline 131 views 46 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fingerprint recognition is a widely adopted biometric technology, valued for its reliability and precision in identifying individuals. However, traditional recognition methods relying on handcrafted features struggle under challenging scenarios such as overpressured fingerprints, where excessive pressure distorts ridge patterns, significantly affecting performance. To address these challenges, this study proposes a novel framework combining domain adaptation techniques and an attention mechanism. The framework aligns feature distributions between source and target domains, enhancing the model's generalizability to diverse datasets and acquisition conditions. Additionally, the attention mechanism emphasizes critical regions of the fingerprint, improving robustness to distortions. Experimental results demonstrate that the proposed model significantly outperforms the original ResNet, achieving a reduced Equal Error Rate (EER) of 0.0837 compared to 0.1840 for the baseline. Grad-CAM visualizations further validate the model's ability to focus on essential fingerprint features, even under distorted conditions. This study highlights the effectiveness of integrating domain adaptation and attention mechanisms in overcoming real-world challenges in fingerprint recognition. Supplementary Material File (4. a novel fingerprint recognition framework with attention mechanism based on domain adaptation for improving applicability in overpressured situations.pdf) Download 1.33 MB Information & Authors Information Version history V1 Version 1 15 May 2025 Peer review timeline Published Artificial Intelligence Advances Version of Record 25 Oct 2024 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning domain adaptation i fingerprint recognition Authors Affiliations Jiahuai Ma 0009-0008-4623-2575 [email protected] View all articles by this author Alan Wilson View all articles by this author Metrics & Citations Metrics Article Usage 131 views 46 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jiahuai Ma, Alan Wilson. A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations. Authorea . 15 May 2025. DOI: https://doi.org/10.22541/au.174733612.27353793/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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