Multi-Gait Recognition Based on Attribute Discovery
Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. Thi...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2018-07, Vol.40 (7), p.1697-1710 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Chen, Xin Weng, Jian Lu, Wei Xu, Jiaming |
| description | Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-by-detection method to obtain a person's complete silhouette. Then, stable and discriminative attributes are developed using a latent conditional random field (L-CRF) model. The model is trained in the latent structural support vector machine (SVM) framework, in which a new constraint is added to improve the multi-gait recognition performance. In the recognition process, the attribute set of each person is detected by inferring on the trained L-CRF model. Finally, attributes based on dense trajectories are extracted as the final gait features to complete the recognition. The experimental results demonstrate that the proposed method achieves better recognition performance than traditional gait recognition methods under the condition of multiple people walking together. |
| doi_str_mv | 10.1109/TPAMI.2017.2726061 |
| format | Article |
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Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-by-detection method to obtain a person's complete silhouette. Then, stable and discriminative attributes are developed using a latent conditional random field (L-CRF) model. The model is trained in the latent structural support vector machine (SVM) framework, in which a new constraint is added to improve the multi-gait recognition performance. In the recognition process, the attribute set of each person is detected by inferring on the trained L-CRF model. Finally, attributes based on dense trajectories are extracted as the final gait features to complete the recognition. The experimental results demonstrate that the proposed method achieves better recognition performance than traditional gait recognition methods under the condition of multiple people walking together.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2017.2726061</identifier><identifier>PMID: 28708545</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adult ; attributes ; Biometric Identification - methods ; Biometrics ; Data privacy ; Data security ; Databases, Factual ; dense trajectories ; Feature extraction ; Feature recognition ; Female ; Gait - physiology ; Gait Analysis - methods ; Gait recognition ; Hidden Markov models ; human graphlets ; Humans ; latent structural SVM ; Legged locomotion ; Male ; Multi-gait recognition ; Pattern Recognition, Automated - methods ; Pose estimation ; Support Vector Machine ; Support vector machines ; Walking ; Young Adult</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2018-07, Vol.40 (7), p.1697-1710</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c510t-b798034e4f3c6bb5a53186362acaee39be382b2067eda0084558406d9d5996c63</citedby><cites>FETCH-LOGICAL-c510t-b798034e4f3c6bb5a53186362acaee39be382b2067eda0084558406d9d5996c63</cites><orcidid>0000-0003-2903-5525 ; 0000-0003-4067-8230 ; 0000-0002-4068-1766</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7976333$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7976333$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28708545$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Weng, Jian</creatorcontrib><creatorcontrib>Lu, Wei</creatorcontrib><creatorcontrib>Xu, Jiaming</creatorcontrib><title>Multi-Gait Recognition Based on Attribute Discovery</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-by-detection method to obtain a person's complete silhouette. Then, stable and discriminative attributes are developed using a latent conditional random field (L-CRF) model. The model is trained in the latent structural support vector machine (SVM) framework, in which a new constraint is added to improve the multi-gait recognition performance. In the recognition process, the attribute set of each person is detected by inferring on the trained L-CRF model. 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The experimental results demonstrate that the proposed method achieves better recognition performance than traditional gait recognition methods under the condition of multiple people walking together.</description><subject>Adult</subject><subject>attributes</subject><subject>Biometric Identification - methods</subject><subject>Biometrics</subject><subject>Data privacy</subject><subject>Data security</subject><subject>Databases, Factual</subject><subject>dense trajectories</subject><subject>Feature extraction</subject><subject>Feature recognition</subject><subject>Female</subject><subject>Gait - physiology</subject><subject>Gait Analysis - methods</subject><subject>Gait recognition</subject><subject>Hidden Markov models</subject><subject>human graphlets</subject><subject>Humans</subject><subject>latent structural SVM</subject><subject>Legged locomotion</subject><subject>Male</subject><subject>Multi-gait recognition</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pose estimation</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Walking</subject><subject>Young Adult</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkF1LwzAUhoMobk7_gIIUvPGmMx_N1-WcOgcbiszrkLZnktG12qTC_r2Zm7swNwnkOS_veRC6JHhICNZ3i9fRfDqkmMghlVRgQY5Qn2imU8aZPkZ9TARNlaKqh868X2FMMo7ZKepRJbHiGe8jNu-q4NKJdSF5g6L5qF1wTZ3cWw9lEh-jEFqXdwGSB-eL5hvazTk6WdrKw8X-HqD3p8fF-DmdvUym49EsLTjBIc2lVphlkC1ZIfKcW86IEkxQW1gApnNgiuYUCwmlxVhlnKsMi1KXXGtRCDZAt7vcz7b56sAHs44VoKpsDU3nDdFxd6011xG9-Yeumq6tYztDicxiH662gXRHFW3jfQtL89m6tW03hmCzVWp-lZqtUrNXGoeu99FdvobyMPLnMAJXO8ABwOFbailYPD_pmHfd</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>Chen, Xin</creator><creator>Weng, Jian</creator><creator>Lu, Wei</creator><creator>Xu, Jiaming</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2903-5525</orcidid><orcidid>https://orcid.org/0000-0003-4067-8230</orcidid><orcidid>https://orcid.org/0000-0002-4068-1766</orcidid></search><sort><creationdate>20180701</creationdate><title>Multi-Gait Recognition Based on Attribute Discovery</title><author>Chen, Xin ; Weng, Jian ; Lu, Wei ; Xu, Jiaming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-b798034e4f3c6bb5a53186362acaee39be382b2067eda0084558406d9d5996c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>attributes</topic><topic>Biometric Identification - methods</topic><topic>Biometrics</topic><topic>Data privacy</topic><topic>Data security</topic><topic>Databases, Factual</topic><topic>dense trajectories</topic><topic>Feature extraction</topic><topic>Feature recognition</topic><topic>Female</topic><topic>Gait - physiology</topic><topic>Gait Analysis - methods</topic><topic>Gait recognition</topic><topic>Hidden Markov models</topic><topic>human graphlets</topic><topic>Humans</topic><topic>latent structural SVM</topic><topic>Legged locomotion</topic><topic>Male</topic><topic>Multi-gait recognition</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Pose estimation</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Walking</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Weng, Jian</creatorcontrib><creatorcontrib>Lu, Wei</creatorcontrib><creatorcontrib>Xu, Jiaming</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</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>Chen, Xin</au><au>Weng, Jian</au><au>Lu, Wei</au><au>Xu, Jiaming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-Gait Recognition Based on Attribute Discovery</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2018-07-01</date><risdate>2018</risdate><volume>40</volume><issue>7</issue><spage>1697</spage><epage>1710</epage><pages>1697-1710</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-by-detection method to obtain a person's complete silhouette. Then, stable and discriminative attributes are developed using a latent conditional random field (L-CRF) model. The model is trained in the latent structural support vector machine (SVM) framework, in which a new constraint is added to improve the multi-gait recognition performance. In the recognition process, the attribute set of each person is detected by inferring on the trained L-CRF model. 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| subjects | Adult attributes Biometric Identification - methods Biometrics Data privacy Data security Databases, Factual dense trajectories Feature extraction Feature recognition Female Gait - physiology Gait Analysis - methods Gait recognition Hidden Markov models human graphlets Humans latent structural SVM Legged locomotion Male Multi-gait recognition Pattern Recognition, Automated - methods Pose estimation Support Vector Machine Support vector machines Walking Young Adult |
| title | Multi-Gait Recognition Based on Attribute Discovery |
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