Deep Learning Model of Latent Fingerprint Identification Using Regularized Gaussian Distributive Gradient-Optimized Vision Transformer

Deep Learning Model of Latent Fingerprint Identification Using Regularized Gaussian Distributive Gradient-Optimized Vision Transformer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deep Learning Model of Latent Fingerprint Identification Using Regularized Gaussian Distributive Gradient-Optimized Vision Transformer Dharmalingam Muthusamy, Saritha Muniyappan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7582713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The conventional methods for latent fingerprint recognition are highly complex to improve accuracy with minimal time consumption. To enhance the accuracy, a novel Regularized Gaussian Distributive Gradient Optimized Vision Transformer (RGDGOVT) model is developed with minimal time consumption. The proposed transformer learning model is a type of deep learning classifier that aims to improve the latent fingerprint identification rate while reducing processing time. The proposed RGDGOVT model consists of distinct processes such as image acquisition, pre-processing, segmentation, feature extraction, and classification. First, the numbers of latent fingerprint images are collected from the dataset in the acquisition phase. Following this, image pre-processing is carried out with a regularized extreme deviate statistical denoising method to enhance the image contrast, resulting in it improving the peak signal-to-noise ratio. The proposed RGDGOVT model undergoes segmentations to extract the region of interest from the input image using the Gaussian distributive contextual graphical method. The segmented region of interest is given to the adaptive gradient-optimized vision transformer model, where the minutiae feature extraction and matching process is carried out. Finally, the matched and unmatched outcomes are obtained. Finally, accurate latent fingerprint identification is achieved at the output layer. The comprehensive experiments are conducted using various metrics such as peak signal-to-noise ratio, identification rate, sensitivity, specificity, confusion matrix, and identification time. The quantitatively analyzed outcomes demonstrate that the proposed RGDGOVT model achieves a better identification rate for latent fingerprints, with improved sensitivity, specificity, and minimum time consumption than the conventional deep learning methods. Latent fingerprint Deep learning Denoising ROI segmentation Vision transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7582713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523104680,"identity":"c7e5a6dc-a7a5-4d2d-a1c3-9f70f53c7a8a","order_by":0,"name":"Dharmalingam 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