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Near Infrared Face Image Quality Assessment System of Video Sequences
In near infrared face recognition systems, situations including head rotation, motion blur, darkness, eyes closed, mouth opened and the small face region will deteriorate the recognition accuracy. Thus, it is necessary to design a quality assessment system to select the best frame from the input vid...
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creator | Jianfeng Long Shutao Li |
description | In near infrared face recognition systems, situations including head rotation, motion blur, darkness, eyes closed, mouth opened and the small face region will deteriorate the recognition accuracy. Thus, it is necessary to design a quality assessment system to select the best frame from the input video sequence before face recognition or saving it to database. In this paper we present a scoring evaluation system based on five features including sharpness, brightness, resolution, head pose and expression. Firstly, the score of each feature is computed independently, and then the final quality score is obtained by combining the scores of five features with weights. Center for Biometrics and Security Research (CBSR) Near Infrared Face Dataset is used to test the system. The experiment results demonstrate the effectiveness of the proposed quality assessment. |
doi_str_mv | 10.1109/ICIG.2011.45 |
format | conference_proceeding |
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Thus, it is necessary to design a quality assessment system to select the best frame from the input video sequence before face recognition or saving it to database. In this paper we present a scoring evaluation system based on five features including sharpness, brightness, resolution, head pose and expression. Firstly, the score of each feature is computed independently, and then the final quality score is obtained by combining the scores of five features with weights. Center for Biometrics and Security Research (CBSR) Near Infrared Face Dataset is used to test the system. The experiment results demonstrate the effectiveness of the proposed quality assessment.</description><identifier>ISBN: 1457715600</identifier><identifier>ISBN: 9781457715600</identifier><identifier>EISBN: 9780769545417</identifier><identifier>EISBN: 0769545416</identifier><identifier>DOI: 10.1109/ICIG.2011.45</identifier><language>eng</language><publisher>IEEE</publisher><subject>Brightness ; Face ; face quality assessment ; Face recognition ; Image resolution ; Mouth ; near infrared ; scoring evaluation system ; Video sequences</subject><ispartof>2011 Sixth International Conference on Image and Graphics, 2011, p.275-279</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6005595$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6005595$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jianfeng Long</creatorcontrib><creatorcontrib>Shutao Li</creatorcontrib><title>Near Infrared Face Image Quality Assessment System of Video Sequences</title><title>2011 Sixth International Conference on Image and Graphics</title><addtitle>icig</addtitle><description>In near infrared face recognition systems, situations including head rotation, motion blur, darkness, eyes closed, mouth opened and the small face region will deteriorate the recognition accuracy. Thus, it is necessary to design a quality assessment system to select the best frame from the input video sequence before face recognition or saving it to database. In this paper we present a scoring evaluation system based on five features including sharpness, brightness, resolution, head pose and expression. Firstly, the score of each feature is computed independently, and then the final quality score is obtained by combining the scores of five features with weights. Center for Biometrics and Security Research (CBSR) Near Infrared Face Dataset is used to test the system. The experiment results demonstrate the effectiveness of the proposed quality assessment.</description><subject>Brightness</subject><subject>Face</subject><subject>face quality assessment</subject><subject>Face recognition</subject><subject>Image resolution</subject><subject>Mouth</subject><subject>near infrared</subject><subject>scoring evaluation system</subject><subject>Video sequences</subject><isbn>1457715600</isbn><isbn>9781457715600</isbn><isbn>9780769545417</isbn><isbn>0769545416</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81Kw0AURkdEUGt27tzMC6Tem8ydn2UJbQ0URVrclklyRyJNqpl0kbc3oGfz7Q7fEeIRYYkI7rksyu0yA8SloiuROGPBaEeKFJprcY-KjEHSALciifELZrR2GeKdWL-yH2TZh8EP3MiNr1mWnf9k-X7xp3ac5CpGjrHjfpT7KY7cyXOQH23DZ7nnnwv3NccHcRP8KXLyvwtx2KwPxUu6e9uWxWqXtg7GtIKcyVqrG2Or-R01TkPuHRlkquqakTVqaLIqhIDG1VBbHSxopTI1N-QL8fSnbZn5-D20nR-m49xF5Cj_BWGtSYc</recordid><startdate>201108</startdate><enddate>201108</enddate><creator>Jianfeng Long</creator><creator>Shutao Li</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201108</creationdate><title>Near Infrared Face Image Quality Assessment System of Video Sequences</title><author>Jianfeng Long ; Shutao Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-b03e58886d78b5455d9603a9571e5bcce1e6160d2bfff179c0c86f80644244573</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Brightness</topic><topic>Face</topic><topic>face quality assessment</topic><topic>Face recognition</topic><topic>Image resolution</topic><topic>Mouth</topic><topic>near infrared</topic><topic>scoring evaluation system</topic><topic>Video sequences</topic><toplevel>online_resources</toplevel><creatorcontrib>Jianfeng Long</creatorcontrib><creatorcontrib>Shutao Li</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 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>Jianfeng Long</au><au>Shutao Li</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Near Infrared Face Image Quality Assessment System of Video Sequences</atitle><btitle>2011 Sixth International Conference on Image and Graphics</btitle><stitle>icig</stitle><date>2011-08</date><risdate>2011</risdate><spage>275</spage><epage>279</epage><pages>275-279</pages><isbn>1457715600</isbn><isbn>9781457715600</isbn><eisbn>9780769545417</eisbn><eisbn>0769545416</eisbn><abstract>In near infrared face recognition systems, situations including head rotation, motion blur, darkness, eyes closed, mouth opened and the small face region will deteriorate the recognition accuracy. Thus, it is necessary to design a quality assessment system to select the best frame from the input video sequence before face recognition or saving it to database. In this paper we present a scoring evaluation system based on five features including sharpness, brightness, resolution, head pose and expression. Firstly, the score of each feature is computed independently, and then the final quality score is obtained by combining the scores of five features with weights. Center for Biometrics and Security Research (CBSR) Near Infrared Face Dataset is used to test the system. The experiment results demonstrate the effectiveness of the proposed quality assessment.</abstract><pub>IEEE</pub><doi>10.1109/ICIG.2011.45</doi><tpages>5</tpages></addata></record> |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Brightness Face face quality assessment Face recognition Image resolution Mouth near infrared scoring evaluation system Video sequences |
title | Near Infrared Face Image Quality Assessment System of Video Sequences |
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