An efficient gravitational search decision forest approach for fingerprint recognition

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> An efficient gravitational search decision forest approach for fingerprint recognition | Kuwait Journal of Science </title> <link rel="icon" href="https://journalskuwait.org/kjs/public/journals/1/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.2.1.1"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Kuwait Journal of Science"/> <meta name="citation_journal_abbrev" content="KJS"/> <meta name="citation_issn" content="2307-4116"/> <meta name="citation_author" content="Mahesh Kumar"/> <meta name="citation_author_institution" content="Dept. of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India"/> <meta name="citation_author" content="Devender Kumar"/> <meta name="citation_author_institution" content="Dept. of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India"/> <meta name="citation_title" content="An efficient gravitational search decision forest approach for fingerprint recognition: DOI : 10.48129/kjs.20635 "/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023/03/10"/> <meta name="citation_volume" content="50"/> <meta name="citation_issue" content="2A"/> <meta name="citation_doi" content="10.48129/kjs.20635"/> <meta name="citation_abstract_html_url" content="https://journalskuwait.org/kjs/index.php/KJS/article/view/20635"/> <meta name="citation_pdf_url" content="https://journalskuwait.org/kjs/index.php/KJS/article/download/20635/1263"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Mahesh Kumar"/> <meta name="DC.Creator.PersonalName" content="Devender Kumar"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-03-10"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-05-24"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-03-10"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2023-03-10"/> <meta name="DC.Description" xml:lang="en" content="Fingerprint based human identification is one of the authentic biometric recognition systems due to the permanence and uniqueness of the finger impressions. There is the extensive usage of fingerprint recognition in personalized electronic devices, security systems, banking, forensic labs, and especially in law enforcement agencies. Although the existing systems can recognize fingerprints, they lack in case of poor quality and latent fingerprints. The latent fingerprints are captured by law enforcement agencies during the crime scene to find the criminal. Consequently, it is essential to develop a novel system that can efficiently recognize both complete and latent fingerprints. The current work proposes an efficient Gravitational Search Decision Forest (GSDF) method, which is a combination of the gravitational search algorithm (GSA) and the random forest (RF) method. In the proposed GSDF approach, the mass agent of GSA determines the solution by constructing decision trees in accordance with the random forest hypothesis. The recognition of the fingerprints is accomplished by mass agents in the form of a final generated decision forest from the set of hypothesis space as the mass agents can create multiple hypotheses using random proportional rules. The experiments for fingerprint recognition are conducted for both the latent fingerprints (NIST SD27 dataset) and the complete fingerprints (FVC2004 dataset). The effectiveness of the proposed GSDF approach is analyzed by evaluating the results with machine learning classifiers (random forest, decision tree, back propagation neural networks, and k-nearest neighbor) as well. The comparative analysis of the proposed approach and incorporated machine learning classifiers indicates the outperformed performance of the proposed approach.&nbsp; &nbsp;"/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="20635"/> <meta name="DC.Identifier.DOI" content="10.48129/kjs.20635"/> <meta name="DC.Identifier.URI" content="https://journalskuwait.org/kjs/index.php/KJS/article/view/20635"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 "/> <meta name="DC.Rights" content=""/> <meta name="DC.Source" content="Kuwait Journal of Science"/> <meta name="DC.Source.ISSN" content="2307-4116"/> <meta name="DC.Source.Issue" content="2A"/> <meta name="DC.Source.Volume" content="50"/> <meta name="DC.Source.URI" content="https://journalskuwait.org/kjs/index.php/KJS"/> <meta name="DC.Title" content="An efficient gravitational search decision forest approach for fingerprint 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<input name="query" value="" type="text" aria-label="Search Query"> <button type="submit"> Search </button> <div class="search_controls" aria-hidden="true"> <a href="https://journalskuwait.org/kjs/index.php/KJS/search/search" class="headerSearchPrompt search_prompt" aria-hidden="true"> Search </a> <a href="#" class="search_cancel headerSearchCancel" aria-hidden="true"></a> <span class="search_loading" aria-hidden="true"></span> </div> </form> </nav> </div> </header> <div class="pkp_structure_content has_sidebar"> <div class="pkp_structure_main" role="main"> <a id="pkp_content_main"></a> <div class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/issue/view/73"> Vol. 50 No. 2A (2023): Kuwait Journal of Science </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Computer Science </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> An efficient gravitational search decision forest approach for fingerprint recognition </h1> <h2 class="subtitle"> DOI : 10.48129/kjs.20635 </h2> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Mahesh Kumar </span> <span class="affiliation"> Dept. of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India </span> </li> <li> <span class="name"> Devender Kumar </span> <span class="affiliation"> Dept. of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.48129/kjs.20635"> https://doi.org/10.48129/kjs.20635 </a> </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Fingerprint based human identification is one of the authentic biometric recognition systems due to the permanence and uniqueness of the finger impressions. There is the extensive usage of fingerprint recognition in personalized electronic devices, security systems, banking, forensic labs, and especially in law enforcement agencies. Although the existing systems can recognize fingerprints, they lack in case of poor quality and latent fingerprints. The latent fingerprints are captured by law enforcement agencies during the crime scene to find the criminal. Consequently, it is essential to develop a novel system that can efficiently recognize both complete and latent fingerprints. The current work proposes an efficient Gravitational Search Decision Forest (GSDF) method, which is a combination of the gravitational search algorithm (GSA) and the random forest (RF) method. In the proposed GSDF approach, the mass agent of GSA determines the solution by constructing decision trees in accordance with the random forest hypothesis. The recognition of the fingerprints is accomplished by mass agents in the form of a final generated decision forest from the set of hypothesis space as the mass agents can create multiple hypotheses using random proportional rules. The experiments for fingerprint recognition are conducted for both the latent fingerprints (NIST SD27 dataset) and the complete fingerprints (FVC2004 dataset). The effectiveness of the proposed GSDF approach is analyzed by evaluating the results with machine learning classifiers (random forest, decision tree, back propagation neural networks, and k-nearest neighbor) as well. The comparative analysis of the proposed approach and incorporated machine learning classifiers indicates the outperformed performance of the proposed approach.聽聽</p> </section> </div> <div class="entry_details"> <div class="item cover_image"> <div class="sub_item"> <a href="https://journalskuwait.org/kjs/index.php/KJS/issue/view/73"> <img src="https://journalskuwait.org/kjs/public/journals/1/cover_issue_73_en_US.jpg" alt=""> </a> </div> </div> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://journalskuwait.org/kjs/index.php/KJS/article/view/20635/1263"> Click Here to Download PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>10-03-2023</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://journalskuwait.org/kjs/index.php/KJS/issue/view/73"> Vol. 50 No. 2A (2023): Kuwait Journal of Science </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Computer Science </div> </section> </div> </div> </div> </article> </div> </div> <div class="pkp_structure_sidebar left" role="complementary" aria-label="Sidebar"> <div class="pkp_block block_information"> <h2 class="title">Information</h2> <div class="content"> <ul> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/information/readers"> For Readers </a> </li> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/information/authors"> For Authors </a> </li> <li> <a href="https://journalskuwait.org/kjs/index.php/KJS/information/librarians"> For Librarians </a> </li> </ul> </div> </div> <div class="pkp_block block_developed_by"> <h2 class="pkp_screen_reader"> Developed By </h2> <div class="content"> <a href="http://pkp.sfu.ca/ojs/"> Open Journal Systems </a> </div> </div> </div> </div> <div class="pkp_structure_footer_wrapper" role="contentinfo"> <a id="pkp_content_footer"></a> <div class="pkp_structure_footer"> <div class="pkp_footer_content"> <div class="custom-footer-row"> <div class="custom-footer-column"><img src="https://journalskuwait.org/kjs/public/site/images/kuwaitjs/foter-log.png" alt="" width="246" height="172" /></div> <div class="custom-footer-column"> <table> <tbody> <tr> <td><img src="https://journalskuwait.org/kjs/public/site/images/kuwaitjs/location.png" width="32" height="32" /></td> <td>P.O. 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An efficient gravitational search decision forest approach for fingerprint recognition | Kuwait Journal of Science