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Remote person authentication in different scenarios based on gait and face in front view
To predict criminal acts and to assure more security, the biometric remote recognition of people has lately been getting much interest among researchers. We propose in this paper to use biometric modalities that may be acquired remotely, which are the gait and the face. The gait is explored at 11 di...
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creator | El Kissi Ghalleb, Asma Ben Amara, Najoua Essoukri |
description | To predict criminal acts and to assure more security, the biometric remote recognition of people has lately been getting much interest among researchers. We propose in this paper to use biometric modalities that may be acquired remotely, which are the gait and the face. The gait is explored at 11 different angles of view with different styles of clothes using the CASIA Gait Datasets A and B. For the front view, we fuse the gait with hard and soft facial biometrics. Tested on both public databases, the gait-based recognition has yielded interesting results in different cases compared to the existing results in the literature. The system based on the fusion of the gait with the face has led to better results. |
doi_str_mv | 10.1109/SSD.2017.8167008 |
format | conference_proceeding |
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We propose in this paper to use biometric modalities that may be acquired remotely, which are the gait and the face. The gait is explored at 11 different angles of view with different styles of clothes using the CASIA Gait Datasets A and B. For the front view, we fuse the gait with hard and soft facial biometrics. Tested on both public databases, the gait-based recognition has yielded interesting results in different cases compared to the existing results in the literature. The system based on the fusion of the gait with the face has led to better results.</description><subject>angle of view</subject><subject>Cameras</subject><subject>data fusion</subject><subject>Face</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>gait</subject><subject>Image color analysis</subject><subject>Legged locomotion</subject><issn>2474-0446</issn><isbn>153863175X</isbn><isbn>9781538631751</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2017</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotUE1LAzEUjIJgrb0LXvIHtr5sPvcoVatQEKxCb-Vt8qIRu1s2UfHfu2JPw3wwDMPYhYC5ENBcrdc38xqEnTthLIA7YmdCS2eksHpzzCa1sqoCpcwpm-X8DgDCCN0oNWGbJ9r1hfiehtx3HD_LG3UleSxppKnjIcVIw6jx7KnDIfWZt5gp8NF_xVQ4doFH9PSXjkM_Jr8SfZ-zk4gfmWYHnLKXu9vnxX21elw-LK5XVRrHlcpSkME7lC4iBB-aiMHqxlvja5Qh2FqigLYlsG1NtRHSBwpt1BCdcFLLKbv8701EtN0PaYfDz_ZwhPwFvSxTfg</recordid><startdate>201703</startdate><enddate>201703</enddate><creator>El Kissi Ghalleb, Asma</creator><creator>Ben Amara, Najoua Essoukri</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201703</creationdate><title>Remote person authentication in different scenarios based on gait and face in front view</title><author>El Kissi Ghalleb, Asma ; Ben Amara, Najoua Essoukri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-7ed3dc8a38fa0dcd9fad759c76c2a3dd723a10bbe07b2e2613cdedbf50f818353</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2017</creationdate><topic>angle of view</topic><topic>Cameras</topic><topic>data fusion</topic><topic>Face</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>gait</topic><topic>Image color analysis</topic><topic>Legged locomotion</topic><toplevel>online_resources</toplevel><creatorcontrib>El Kissi Ghalleb, Asma</creatorcontrib><creatorcontrib>Ben Amara, Najoua Essoukri</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>El Kissi Ghalleb, Asma</au><au>Ben Amara, Najoua Essoukri</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Remote person authentication in different scenarios based on gait and face in front view</atitle><btitle>2017 14th International Multi-Conference on Systems, Signals & Devices (SSD)</btitle><stitle>SSD</stitle><date>2017-03</date><risdate>2017</risdate><spage>486</spage><epage>491</epage><pages>486-491</pages><eissn>2474-0446</eissn><eisbn>153863175X</eisbn><eisbn>9781538631751</eisbn><abstract>To predict criminal acts and to assure more security, the biometric remote recognition of people has lately been getting much interest among researchers. We propose in this paper to use biometric modalities that may be acquired remotely, which are the gait and the face. The gait is explored at 11 different angles of view with different styles of clothes using the CASIA Gait Datasets A and B. For the front view, we fuse the gait with hard and soft facial biometrics. Tested on both public databases, the gait-based recognition has yielded interesting results in different cases compared to the existing results in the literature. The system based on the fusion of the gait with the face has led to better results.</abstract><pub>IEEE</pub><doi>10.1109/SSD.2017.8167008</doi><tpages>6</tpages></addata></record> |
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identifier | EISSN: 2474-0446 |
ispartof | 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), 2017, p.486-491 |
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language | eng |
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source | IEEE Xplore All Conference Series |
subjects | angle of view Cameras data fusion Face Face recognition Feature extraction gait Image color analysis Legged locomotion |
title | Remote person authentication in different scenarios based on gait and face in front view |
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