Automatic Facial Feature Correspondence Based on Pose Estimation
Establishing facial feature correspondence across image of the same subject with different poses is an essential issue in the field of face image interpretation. Traditional approaches involve tedious landmark labeling and time-consuming training process. Instead, an automatic facial feature corresp...
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| creator | Ying Chen Chunjian Hua |
| description | Establishing facial feature correspondence across image of the same subject with different poses is an essential issue in the field of face image interpretation. Traditional approaches involve tedious landmark labeling and time-consuming training process. Instead, an automatic facial feature correspondence method is proposed in the paper, which achieves accurate feature correspondence via sparse feature matching using unary and geometric constraints, and intelligent interpolation based on facial pose angle estimation. The experiments show the validity of the proposed method. |
| doi_str_mv | 10.1109/ETCS.2010.614 |
| format | Conference Proceeding |
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Traditional approaches involve tedious landmark labeling and time-consuming training process. Instead, an automatic facial feature correspondence method is proposed in the paper, which achieves accurate feature correspondence via sparse feature matching using unary and geometric constraints, and intelligent interpolation based on facial pose angle estimation. The experiments show the validity of the proposed method.</description><identifier>ISBN: 1424463882</identifier><identifier>ISBN: 9781424463886</identifier><identifier>EISBN: 1424463890</identifier><identifier>EISBN: 9781424463893</identifier><identifier>DOI: 10.1109/ETCS.2010.614</identifier><identifier>LCCN: 2010900625</identifier><language>eng</language><publisher>IEEE</publisher><subject>Active shape model ; Computer vision ; Detectors ; Eyes ; Face detection ; Facial Feature annotation ; Facial features ; Feature extraction ; graph matching ; Interpolation ; Mouth ; Nose</subject><ispartof>2010 Second International Workshop on Education Technology and Computer Science, 2010, Vol.3, p.153-156</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/5459740$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5459740$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ying Chen</creatorcontrib><creatorcontrib>Chunjian Hua</creatorcontrib><title>Automatic Facial Feature Correspondence Based on Pose Estimation</title><title>2010 Second International Workshop on Education Technology and Computer Science</title><addtitle>ETCS</addtitle><description>Establishing facial feature correspondence across image of the same subject with different poses is an essential issue in the field of face image interpretation. Traditional approaches involve tedious landmark labeling and time-consuming training process. Instead, an automatic facial feature correspondence method is proposed in the paper, which achieves accurate feature correspondence via sparse feature matching using unary and geometric constraints, and intelligent interpolation based on facial pose angle estimation. The experiments show the validity of the proposed method.</description><subject>Active shape model</subject><subject>Computer vision</subject><subject>Detectors</subject><subject>Eyes</subject><subject>Face detection</subject><subject>Facial Feature annotation</subject><subject>Facial features</subject><subject>Feature extraction</subject><subject>graph matching</subject><subject>Interpolation</subject><subject>Mouth</subject><subject>Nose</subject><isbn>1424463882</isbn><isbn>9781424463886</isbn><isbn>1424463890</isbn><isbn>9781424463893</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFTs9LwzAYjchAN3f05CX_QOeX9Eua3JxlVWGg4O4jTb5AZTaj6Q7-93Yo7F0eD94vxu4FrIQA-7jZ1Z8rCZPUAq_YXKBE1KWxcH0RRs7Y_GyyAFqqG7bM-QsmoJJamFv2tD6N6duNneeN85078IbceBqI12kYKB9TH6j3xJ9dpsBTzz9SJr7JY3dOpf6OzaI7ZFr-84Ltmunaa7F9f3mr19uiszAWQXjhPFTCxBa9daBQVhgjKqFikC2V6FpjWiTro_KoJx-IABoFxaBsuWAPf7UdEe2Pw7Q-_OwVKlshlL8PVErp</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Ying Chen</creator><creator>Chunjian Hua</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>Automatic Facial Feature Correspondence Based on Pose Estimation</title><author>Ying Chen ; Chunjian Hua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-d1c1ac0718fb4c9a054274ff4515fd2be34ab88b4e9cf5c468fb01d0641efd593</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Active shape model</topic><topic>Computer vision</topic><topic>Detectors</topic><topic>Eyes</topic><topic>Face detection</topic><topic>Facial Feature annotation</topic><topic>Facial features</topic><topic>Feature extraction</topic><topic>graph matching</topic><topic>Interpolation</topic><topic>Mouth</topic><topic>Nose</topic><toplevel>online_resources</toplevel><creatorcontrib>Ying Chen</creatorcontrib><creatorcontrib>Chunjian Hua</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>Ying Chen</au><au>Chunjian Hua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic Facial Feature Correspondence Based on Pose Estimation</atitle><btitle>2010 Second International Workshop on Education Technology and Computer Science</btitle><stitle>ETCS</stitle><date>2010-03</date><risdate>2010</risdate><volume>3</volume><spage>153</spage><epage>156</epage><pages>153-156</pages><isbn>1424463882</isbn><isbn>9781424463886</isbn><eisbn>1424463890</eisbn><eisbn>9781424463893</eisbn><abstract>Establishing facial feature correspondence across image of the same subject with different poses is an essential issue in the field of face image interpretation. Traditional approaches involve tedious landmark labeling and time-consuming training process. Instead, an automatic facial feature correspondence method is proposed in the paper, which achieves accurate feature correspondence via sparse feature matching using unary and geometric constraints, and intelligent interpolation based on facial pose angle estimation. The experiments show the validity of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/ETCS.2010.614</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Active shape model Computer vision Detectors Eyes Face detection Facial Feature annotation Facial features Feature extraction graph matching Interpolation Mouth Nose |
| title | Automatic Facial Feature Correspondence Based on Pose Estimation |
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