A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆ | 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 A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆ Manu Shree, Amar Kumar Mohapatra, Hemmaphan Suwanwiwat, Virendra P. Vishwakarma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4250349/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 In suspect identification systems, facial features play a crucial role in recognising individuals. However, the challenge lies in sustaining the accuracy of the system over a long period of time, ensuring that it remains consistently high, reliable, and effective. This research introduces a novel lightweight model that requires low trainable parameters, a significantly smaller number than pre-trained models, which use millions of trainable parameters. The newly proposed recurrent feature iterative network integrates a convolutional neural network and long short-term memory in a single structure to synthesise diverse images and to effectively extract facial features. The long short-term memory-based feature-recurrent system demonstrates a significant improvement in accuracy when tested on the augmented reality face database, enhanced extended Yale B, Cohn-Kanade and extended Yale B datasets, achieving encouraging accuracy rates of 99.23%, 99.16%, 98.99%, and 97.68%, respectively. These accuracies, in general, outperform traditional baselines of 68.65%. This research advances the field by providing an innovative solution to enhance suspect identification systems through advanced facial image feature extraction, resulting in significantly improved accuracy rates. Suspect Identification Feature Extraction Long Short-Term Memory(LSTM) ConvolutionalNeuralNetwork (CNN) 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-4250349","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290567808,"identity":"9e9211c7-8da6-4cd1-befe-cd8757a913e0","order_by":0,"name":"Manu Shree","email":"","orcid":"","institution":"Indira Gandhi Delhi Technical University for Women","correspondingAuthor":false,"prefix":"","firstName":"Manu","middleName":"","lastName":"Shree","suffix":""},{"id":290567811,"identity":"10b2f1fd-d9fb-4452-9879-2d23d88e95ca","order_by":1,"name":"Amar Kumar Mohapatra","email":"","orcid":"","institution":"Indira Gandhi Delhi Technical University for Women","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"Kumar","lastName":"Mohapatra","suffix":""},{"id":290567814,"identity":"4686a27f-9eee-4f1e-a68d-7d6578d1cac0","order_by":2,"name":"Hemmaphan Suwanwiwat","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Hemmaphan","middleName":"","lastName":"Suwanwiwat","suffix":""},{"id":290567815,"identity":"530ba106-997f-4a9b-ba43-540732b3b359","order_by":3,"name":"Virendra P. 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