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Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition

Ruben Vera-Rodriguez, John Mason, Julian Fierrez, Javier Ortega-Garcia

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 35, Issue: 4, Pages: 823 - 834

Swansea University Author: John Mason

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DOI (Published version): 10.1109/tpami.2012.164

Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN: 0162-8828 2160-9292
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa12735
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first_indexed 2013-07-23T12:08:39Z
last_indexed 2018-02-09T04:43:07Z
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2016-08-17T13:51:58.9032591</datestamp><bib-version>v2</bib-version><id>12735</id><entry>2013-09-03</entry><title>Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition</title><swanseaauthors><author><sid>284b34c63a5cbc71055047daf2ee1392</sid><firstname>John</firstname><surname>Mason</surname><name>John Mason</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2013-09-03</date><deptcode>EEN</deptcode><abstract></abstract><type>Journal Article</type><journal>IEEE Transactions on Pattern Analysis and Machine Intelligence</journal><volume>35</volume><journalNumber>4</journalNumber><paginationStart>823</paginationStart><paginationEnd>834</paginationEnd><publisher/><issnPrint>0162-8828</issnPrint><issnElectronic>2160-9292</issnElectronic><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2013</publishedYear><publishedDate>2013-12-31</publishedDate><doi>10.1109/tpami.2012.164</doi><url/><notes>Footstep recognition is a relatively new biometric, which aims to discriminate persons using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatio-temporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons. Results show very similar performance for both spatial and temporal approaches (5% to 15% EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5% to 10% EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.</notes><college>COLLEGE NANME</college><department>Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EEN</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2016-08-17T13:51:58.9032591</lastEdited><Created>2013-09-03T06:00:26.0000000</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Ruben</firstname><surname>Vera-Rodriguez</surname><order>1</order></author><author><firstname>John</firstname><surname>Mason</surname><order>2</order></author><author><firstname>Julian</firstname><surname>Fierrez</surname><order>3</order></author><author><firstname>Javier</firstname><surname>Ortega-Garcia</surname><order>4</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2016-08-17T13:51:58.9032591 v2 12735 2013-09-03 Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition 284b34c63a5cbc71055047daf2ee1392 John Mason John Mason true false 2013-09-03 EEN Journal Article IEEE Transactions on Pattern Analysis and Machine Intelligence 35 4 823 834 0162-8828 2160-9292 31 12 2013 2013-12-31 10.1109/tpami.2012.164 Footstep recognition is a relatively new biometric, which aims to discriminate persons using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatio-temporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons. Results show very similar performance for both spatial and temporal approaches (5% to 15% EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5% to 10% EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios. COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2016-08-17T13:51:58.9032591 2013-09-03T06:00:26.0000000 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Ruben Vera-Rodriguez 1 John Mason 2 Julian Fierrez 3 Javier Ortega-Garcia 4
title Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
spellingShingle Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
John Mason
title_short Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
title_full Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
title_fullStr Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
title_full_unstemmed Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
title_sort Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
author_id_str_mv 284b34c63a5cbc71055047daf2ee1392
author_id_fullname_str_mv 284b34c63a5cbc71055047daf2ee1392_***_John Mason
author John Mason
author2 Ruben Vera-Rodriguez
John Mason
Julian Fierrez
Javier Ortega-Garcia
format Journal article
container_title IEEE Transactions on Pattern Analysis and Machine Intelligence
container_volume 35
container_issue 4
container_start_page 823
publishDate 2013
institution Swansea University
issn 0162-8828
2160-9292
doi_str_mv 10.1109/tpami.2012.164
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
published_date 2013-12-31T03:14:38Z
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score 11.036116

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