Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recogni...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2013-04, Vol.35 (4), p.823-834 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Vera-Rodriguez, R. Mason, J. S. D. Fierrez, J. Ortega-Garcia, J. |
| description | Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal 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 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent 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. |
| doi_str_mv | 10.1109/TPAMI.2012.164 |
| format | Article |
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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.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2012.164</identifier><identifier>PMID: 22868647</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Biomechanical Phenomena - physiology ; Biometric Identification - instrumentation ; Biometric Identification - methods ; Biometrics ; Computer science; control theory; systems ; Computer systems and distributed systems. 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S. D.</creatorcontrib><creatorcontrib>Fierrez, J.</creatorcontrib><creatorcontrib>Ortega-Garcia, J.</creatorcontrib><title>Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal 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 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent 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.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biomechanical Phenomena - physiology</subject><subject>Biometric Identification - instrumentation</subject><subject>Biometric Identification - methods</subject><subject>Biometrics</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Foot - physiology</subject><subject>footstep recognition</subject><subject>Gait - physiology</subject><subject>gait recognition</subject><subject>Humans</subject><subject>Intelligent sensors</subject><subject>Legged locomotion</subject><subject>Models, Biological</subject><subject>pattern recognition</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Pressure</subject><subject>pressure analysis</subject><subject>Sensor fusion</subject><subject>Sensor phenomena and characterization</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Software</subject><subject>Walking</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpFkE1r20AQhpfSkLhJrr0Uyl4KucjZD-3X0Zg4NSQktEmvYrSaLSqSVt2VA_n3lWs3Pc0w7zPv4SHkI2dLzpm7fnpc3W-XgnGx5Lp8RxbcSVdIJd17smBci8JaYc_Ih5x_McZLxeQpORPCaqtLsyA_1rEfIcHUviBdDdC95jZTGBq62eU2DjQG-n2c4zhhP8YEHd0OIaZ-fxrovNFNjFOecKTf0MefQ7sPLshJgC7j5XGek-fNzdP6a3H3cLtdr-4KL62aCo2NUIEHgEY1gSkl6lIHUVtfusZ4XwtkgaMtvRY1KjBKSwUOvNeAdXDynFwdescUf-8wT1XfZo9dBwPGXa641EoxW1o9o8sD6lPMOWGoxtT2kF4rzqq9y-qvy2rvsppdzg-fj927usfmDf8nbwa-HAHIHrqQYPBt_s8ZbYxxYuY-HbgWEd9iLZSRzsg_M8KHRA</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>Vera-Rodriguez, R.</creator><creator>Mason, J. 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D. ; Fierrez, J. ; Ortega-Garcia, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-6ed25f1faad5df0552b46f2b8c49d7ccb2e0f1e84c62be5a75635a9acc6aebf93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Biomechanical Phenomena - physiology</topic><topic>Biometric Identification - instrumentation</topic><topic>Biometric Identification - methods</topic><topic>Biometrics</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>Foot - physiology</topic><topic>footstep recognition</topic><topic>Gait - physiology</topic><topic>gait recognition</topic><topic>Humans</topic><topic>Intelligent sensors</topic><topic>Legged locomotion</topic><topic>Models, Biological</topic><topic>pattern recognition</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Pressure</topic><topic>pressure analysis</topic><topic>Sensor fusion</topic><topic>Sensor phenomena and characterization</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Software</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vera-Rodriguez, R.</creatorcontrib><creatorcontrib>Mason, J. S. D.</creatorcontrib><creatorcontrib>Fierrez, J.</creatorcontrib><creatorcontrib>Ortega-Garcia, J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Vera-Rodriguez, R.</au><au>Mason, J. S. D.</au><au>Fierrez, J.</au><au>Ortega-Garcia, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2013-04-01</date><risdate>2013</risdate><volume>35</volume><issue>4</issue><spage>823</spage><epage>834</epage><pages>823-834</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal 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 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent 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.</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>22868647</pmid><doi>10.1109/TPAMI.2012.164</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Applied sciences Artificial intelligence Biomechanical Phenomena - physiology Biometric Identification - instrumentation Biometric Identification - methods Biometrics Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Feature extraction Foot - physiology footstep recognition Gait - physiology gait recognition Humans Intelligent sensors Legged locomotion Models, Biological pattern recognition Pattern recognition. Digital image processing. Computational geometry Pressure pressure analysis Sensor fusion Sensor phenomena and characterization Signal Processing, Computer-Assisted Software Walking |
| title | Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition |
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