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Gait Biometric for Person Re-Identification

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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>Gait Biometric for Person Re-Identification</title> <meta name="description" content="Gait Biometric for Person Re-Identification"> <meta name="keywords" content="biometric, gait, silhouettes, You Only Look Once"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Gait Biometric for Person Re-Identification"> <meta name="citation_author" content="Lavanya Srinivasan"> <meta name="citation_publication_date" content="2021/12/14"> <meta name="citation_journal_title" content="International Journal of Biomedical and Biological Engineering"> <meta name="citation_volume" content="15"> <meta name="citation_issue" content="12"> <meta name="citation_firstpage" content="326"> <meta name="citation_lastpage" content="330"> <meta name="citation_pdf_url" 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fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat and case recorded using long wave infrared, short wave infrared, medium wave infrared and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using You Only Look Once, background subtraction, silhouettes extraction and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the Principal Component Analysis and recognized using different classifiers. The comparative results with the different classifier show that Linear Discriminant Analysis outperform other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.</p> <iframe src="https://publications.waset.org/10012344.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=biometric" title="biometric">biometric</a>, <a href="https://publications.waset.org/search?q=gait" title=" gait"> gait</a>, <a href="https://publications.waset.org/search?q=silhouettes" title=" silhouettes"> silhouettes</a>, <a href="https://publications.waset.org/search?q=You%20Only%20Look%20Once" title=" You Only Look Once"> You Only Look Once</a> </p> <a href="https://publications.waset.org/10012344/gait-biometric-for-person-re-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012344/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012344/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012344/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012344/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012344/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012344/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012344/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012344/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012344/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012344/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012344.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">531</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] N. 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Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li. “Yfcc100m: The new data in multimedia research”, Communications of the ACM, pp.59(2):64–73, 2016. <br>[7] Joseph Redmon, Ali Farhadi, “YOLO9000: Better, Faster, Stronger”, Computer Vision and Pattern Recognition, 2016. <br>[8] Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick, “Mask R-CNN”, Computer Vision and Pattern Recognition, 2018. <br>[9] Friedman, N., Russell, S., “Image segmentation in video sequences: a probabilistic approach”, In: Proc. 13th Conf. on Uncertainty in Artificial Intelligence, 1997. <br>[10] Stauffer, C., Grimson, W., “Adaptive background mixture models for real-time tracking”, In: Proc. of the Conf. on Computer Vision and Pattern Recognition. pp. 246–252, 1999. <br>[11] Power, P.W., Schoonees, J.A., “Understanding background mixture models for foreground segmentation”, In: Proc. of the Image and Vision Computing New Zealand, 2002. <br>[12] D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russel. “Towards robust automatic traffic scene analysis in real-time”, In Proc. of the International Conference on Pattern Recognition, Israel, November 1994. <br>[13] Christof Ridder, Olaf Munkelt, and Harald Kirchner “Adaptive Background Estimation and Foreground Detection using Kalman-Filtering,” Proceedings of International Conference on recent Advances in Mechatronics, ICRAM’95, UNESCO Chair on Mechatronics, pp. 193-199, 1995. <br>[14] O. Barnich, M. Van Droogenbroeck, “ViBe: A universal background subtraction algorithm for video sequences”, IEEE Transactions on Image Processing, 20(6), pp. 1709-1724, June 2011. <br>[15] K. Bashir, T. Xiang, S. 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Fry, “Regression Linear Models in Statistics”, Springer Undergraduate Mathematics Series, 2010. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">&copy; 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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