Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers
When first introduced, the cross-ratio ( CR ) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current CR -base...
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| Veröffentlicht in: | International journal of computer vision 2013-02, Vol.101 (3), p.459-481 |
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| container_title | International journal of computer vision |
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| creator | Coutinho, Flavio L. Morimoto, Carlos H. |
| description | When first introduced, the cross-ratio (
CR
) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current
CR
-based methods are still sensitive to head movements. In this paper, we revisit the
CR
-based method and introduce two new extensions to improve the robustness of the method to head motion. The first method dynamically compensates for scale changes in the corneal reflection pattern, and the second method estimates true coplanar eye features so that the cross-ratio can be applied. We present real-time implementations of both systems, and compare the performance of these new methods using simulations and user experiments. Our results show a significant improvement in robustness to head motion and, for the user experiments in particular, an average reduction of up to 40 % in gaze estimation error was observed. |
| doi_str_mv | 10.1007/s11263-012-0541-8 |
| format | Article |
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CR
) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current
CR
-based methods are still sensitive to head movements. In this paper, we revisit the
CR
-based method and introduce two new extensions to improve the robustness of the method to head motion. The first method dynamically compensates for scale changes in the corneal reflection pattern, and the second method estimates true coplanar eye features so that the cross-ratio can be applied. We present real-time implementations of both systems, and compare the performance of these new methods using simulations and user experiments. Our results show a significant improvement in robustness to head motion and, for the user experiments in particular, an average reduction of up to 40 % in gaze estimation error was observed.</description><identifier>ISSN: 0920-5691</identifier><identifier>EISSN: 1573-1405</identifier><identifier>DOI: 10.1007/s11263-012-0541-8</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Accuracy ; Artificial Intelligence ; Calibration ; Cameras ; Chromium ; Computer Imaging ; Computer Science ; Computer simulation ; Cornea ; Dynamical systems ; Estimates ; Eye movements ; Geometry ; Head motions ; Head movement ; Headgear ; Image Processing and Computer Vision ; Interactive computer systems ; International ; Iris ; Methods ; Motion control ; Pattern Recognition ; Pattern Recognition and Graphics ; Reduction ; Robustness ; Studies ; Vision</subject><ispartof>International journal of computer vision, 2013-02, Vol.101 (3), p.459-481</ispartof><rights>Springer Science+Business Media, LLC 2012</rights><rights>COPYRIGHT 2013 Springer</rights><rights>Springer Science+Business Media New York 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c422t-ecfdccf6fdada9d3699beea7b2782aa31ddc19ee06e7600bb97397ca9b83e6bc3</citedby><cites>FETCH-LOGICAL-c422t-ecfdccf6fdada9d3699beea7b2782aa31ddc19ee06e7600bb97397ca9b83e6bc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11263-012-0541-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11263-012-0541-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Coutinho, Flavio L.</creatorcontrib><creatorcontrib>Morimoto, Carlos H.</creatorcontrib><title>Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers</title><title>International journal of computer vision</title><addtitle>Int J Comput Vis</addtitle><description>When first introduced, the cross-ratio (
CR
) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current
CR
-based methods are still sensitive to head movements. In this paper, we revisit the
CR
-based method and introduce two new extensions to improve the robustness of the method to head motion. The first method dynamically compensates for scale changes in the corneal reflection pattern, and the second method estimates true coplanar eye features so that the cross-ratio can be applied. We present real-time implementations of both systems, and compare the performance of these new methods using simulations and user experiments. Our results show a significant improvement in robustness to head motion and, for the user experiments in particular, an average reduction of up to 40 % in gaze estimation error was observed.</description><subject>Accuracy</subject><subject>Artificial Intelligence</subject><subject>Calibration</subject><subject>Cameras</subject><subject>Chromium</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Cornea</subject><subject>Dynamical systems</subject><subject>Estimates</subject><subject>Eye movements</subject><subject>Geometry</subject><subject>Head motions</subject><subject>Head movement</subject><subject>Headgear</subject><subject>Image Processing and Computer Vision</subject><subject>Interactive computer systems</subject><subject>International</subject><subject>Iris</subject><subject>Methods</subject><subject>Motion control</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Reduction</subject><subject>Robustness</subject><subject>Studies</subject><subject>Vision</subject><issn>0920-5691</issn><issn>1573-1405</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kcFq3DAQhkVpoNukD9CboZf2oHRGsi3rmCxJs5AQSLdnIUvjxaltJZI3JG8fLe6hKRQdBsT3Db_0M_YZ4RQB1PeEKGrJAQWHqkTevGMrrJTkWEL1nq1AC-BVrfED-5jSPQCIRsgVW2_Ghxie-mlXXJH1xU14opGmudiGgaKdHBWhK9YxpMTv7NyH4twm8sXFCxXbaN1viumEHXV2SPTpzzxmvy4vtusrfn37Y7M-u-auFGLm5DrvXFd33nqrvay1bomsaoVqhLUSvXeoiaAmVQO0rVZSK2d120iqWyeP2ddlb078uKc0m7FPjobBThT2yWCtUJZNI2VGv_yD3od9nHI6g6KpJGpZQqZOF2pnBzL91IU5PykfT2PvwkRdn-_PpFSgEKsqC9_eCJmZ6Xne2X1KZvPz7i2LC-sOnxepMw-xH218MQjm0JlZOjO5M3PozDTZEYuTMjvtKP4V-7_SK1hil6M</recordid><startdate>20130201</startdate><enddate>20130201</enddate><creator>Coutinho, Flavio L.</creator><creator>Morimoto, Carlos H.</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20130201</creationdate><title>Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers</title><author>Coutinho, Flavio L. ; Morimoto, Carlos H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-ecfdccf6fdada9d3699beea7b2782aa31ddc19ee06e7600bb97397ca9b83e6bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Artificial Intelligence</topic><topic>Calibration</topic><topic>Cameras</topic><topic>Chromium</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Cornea</topic><topic>Dynamical systems</topic><topic>Estimates</topic><topic>Eye movements</topic><topic>Geometry</topic><topic>Head motions</topic><topic>Head movement</topic><topic>Headgear</topic><topic>Image Processing and Computer Vision</topic><topic>Interactive computer systems</topic><topic>International</topic><topic>Iris</topic><topic>Methods</topic><topic>Motion control</topic><topic>Pattern Recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Reduction</topic><topic>Robustness</topic><topic>Studies</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Coutinho, Flavio L.</creatorcontrib><creatorcontrib>Morimoto, Carlos H.</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</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>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of computer vision</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Coutinho, Flavio L.</au><au>Morimoto, Carlos H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers</atitle><jtitle>International journal of computer vision</jtitle><stitle>Int J Comput Vis</stitle><date>2013-02-01</date><risdate>2013</risdate><volume>101</volume><issue>3</issue><spage>459</spage><epage>481</epage><pages>459-481</pages><issn>0920-5691</issn><eissn>1573-1405</eissn><abstract>When first introduced, the cross-ratio (
CR
) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current
CR
-based methods are still sensitive to head movements. In this paper, we revisit the
CR
-based method and introduce two new extensions to improve the robustness of the method to head motion. The first method dynamically compensates for scale changes in the corneal reflection pattern, and the second method estimates true coplanar eye features so that the cross-ratio can be applied. We present real-time implementations of both systems, and compare the performance of these new methods using simulations and user experiments. Our results show a significant improvement in robustness to head motion and, for the user experiments in particular, an average reduction of up to 40 % in gaze estimation error was observed.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11263-012-0541-8</doi><tpages>23</tpages></addata></record> |
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| subjects | Accuracy Artificial Intelligence Calibration Cameras Chromium Computer Imaging Computer Science Computer simulation Cornea Dynamical systems Estimates Eye movements Geometry Head motions Head movement Headgear Image Processing and Computer Vision Interactive computer systems International Iris Methods Motion control Pattern Recognition Pattern Recognition and Graphics Reduction Robustness Studies Vision |
| title | Improving Head Movement Tolerance of Cross-Ratio Based Eye Trackers |
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