On edge structure based adaptive observation model for facial feature tracking
Facial feature tracking is a crucial and challenging task in computer vision. Recently online-learning methods have become increasingly popular on account of their strong ability to adapt to variations and have achieved good results in tracking. However, all previous work used only raw intensity to...
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| creator | Xiaoyan Wang Yangsheng Wang Xuetao Feng Mingcai Zhou |
| description | Facial feature tracking is a crucial and challenging task in computer vision. Recently online-learning methods have become increasingly popular on account of their strong ability to adapt to variations and have achieved good results in tracking. However, all previous work used only raw intensity to build the model, which is very sensitive to condition changes. In this work, we present a real time, fully automatic facial feature detection and tracking approach using adaptive observation models based on edge structure, which is more reliable especially when the lighting state alters during tracking. Experimental results demonstrate that using edge map measures in observation modeling can improve the accuracy and robustness of tracking. |
| doi_str_mv | 10.1109/ICPR.2008.4761151 |
| format | Conference Proceeding |
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Recently online-learning methods have become increasingly popular on account of their strong ability to adapt to variations and have achieved good results in tracking. However, all previous work used only raw intensity to build the model, which is very sensitive to condition changes. In this work, we present a real time, fully automatic facial feature detection and tracking approach using adaptive observation models based on edge structure, which is more reliable especially when the lighting state alters during tracking. 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Experimental results demonstrate that using edge map measures in observation modeling can improve the accuracy and robustness of tracking.</description><subject>Application software</subject><subject>Automation</subject><subject>Computer graphics</subject><subject>Computer vision</subject><subject>Deformable models</subject><subject>Face detection</subject><subject>Facial features</subject><subject>Image edge detection</subject><subject>Robustness</subject><subject>Target tracking</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>9781424421749</isbn><isbn>1424421748</isbn><isbn>9781424421756</isbn><isbn>1424421756</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkFtLAzEUhOMNrLU_QHzJH9iak8smeZTipVCsSN_LSXK2RNtuyW4L_nuL9sWnGRi-gRnG7kCMAYR_mE7eP8ZSCDfWtgYwcMZG3jrQUmsJ1tTnbCCdgspqay7-ZdpfsgEIA5WuDVyzm677FEIKZdyAvc23nNKKeNeXfez3hXjAjhLHhLs-H4i3oaNywD63W75pE6150xbeYMx4tIS_TF8wfuXt6pZdNbjuaHTSIVs8Py0mr9Vs_jKdPM6q7EVfBRUQmySiaqKuI4ZgUzhu8LI20tRoGkzJGRuQhPckoNYUgxNORatAGTVk93-1mYiWu5I3WL6Xp2fUD1T1VAA</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Xiaoyan Wang</creator><creator>Yangsheng Wang</creator><creator>Xuetao Feng</creator><creator>Mingcai Zhou</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>On edge structure based adaptive observation model for facial feature tracking</title><author>Xiaoyan Wang ; Yangsheng Wang ; Xuetao Feng ; Mingcai Zhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-b3baafd0c3fc46cabb7db7569265256a5fadd857bae099e0164ecb8083c731353</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Application software</topic><topic>Automation</topic><topic>Computer graphics</topic><topic>Computer vision</topic><topic>Deformable models</topic><topic>Face detection</topic><topic>Facial features</topic><topic>Image edge detection</topic><topic>Robustness</topic><topic>Target tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaoyan Wang</creatorcontrib><creatorcontrib>Yangsheng Wang</creatorcontrib><creatorcontrib>Xuetao Feng</creatorcontrib><creatorcontrib>Mingcai Zhou</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 Xplore</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>Xiaoyan Wang</au><au>Yangsheng Wang</au><au>Xuetao Feng</au><au>Mingcai Zhou</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>On edge structure based adaptive observation model for facial feature tracking</atitle><btitle>2008 19th International Conference on Pattern Recognition</btitle><stitle>ICPR</stitle><date>2008-12</date><risdate>2008</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>9781424421749</isbn><isbn>1424421748</isbn><eisbn>9781424421756</eisbn><eisbn>1424421756</eisbn><abstract>Facial feature tracking is a crucial and challenging task in computer vision. Recently online-learning methods have become increasingly popular on account of their strong ability to adapt to variations and have achieved good results in tracking. However, all previous work used only raw intensity to build the model, which is very sensitive to condition changes. In this work, we present a real time, fully automatic facial feature detection and tracking approach using adaptive observation models based on edge structure, which is more reliable especially when the lighting state alters during tracking. Experimental results demonstrate that using edge map measures in observation modeling can improve the accuracy and robustness of tracking.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2008.4761151</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2008 19th International Conference on Pattern Recognition, 2008, p.1-4 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Automation Computer graphics Computer vision Deformable models Face detection Facial features Image edge detection Robustness Target tracking |
| title | On edge structure based adaptive observation model for facial feature tracking |
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