{"id":"https://openalex.org/W2737029439","doi":"https://doi.org/10.1109/icra.2017.7989194","title":"Two-eye model-based gaze estimation from a Kinect sensor","display_name":"Two-eye model-based gaze estimation from a Kinect sensor","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2737029439","doi":"https://doi.org/10.1109/icra.2017.7989194","mag":"2737029439"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2017.7989194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://researchportal.port.ac.uk/portal/en/publications/twoeye-modelbased-gaze-estimation-from-a-kinect-sensor(4c61c758-59d5-4a09-b29d-ce6a3c8b9d36).html","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036387111","display_name":"Xiaolong Zhou","orcid":"https://orcid.org/0000-0003-0732-5169"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Zhou","raw_affiliation_strings":["The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077762860","display_name":"Haibin Cai","orcid":"https://orcid.org/0000-0002-2759-3665"},"institutions":[{"id":"https://openalex.org/I63072094","display_name":"University of Portsmouth","ror":"https://ror.org/03ykbk197","country_code":"GB","type":"education","lineage":["https://openalex.org/I63072094"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Haibin Cai","raw_affiliation_strings":["The School of Computing, University of Portsmouth, Portsmouth, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The School of Computing, University of Portsmouth, Portsmouth, UK","institution_ids":["https://openalex.org/I63072094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642147","display_name":"Youfu Li","orcid":"https://orcid.org/0000-0002-5227-1326"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Youfu Li","raw_affiliation_strings":["The Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085867312","display_name":"Honghai Liu","orcid":"https://orcid.org/0000-0002-2880-4698"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I63072094","display_name":"University of Portsmouth","ror":"https://ror.org/03ykbk197","country_code":"GB","type":"education","lineage":["https://openalex.org/I63072094"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Honghai Liu","raw_affiliation_strings":["The School of Computing, University of Portsmouth, Portsmouth, UK","The State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The School of Computing, University of Portsmouth, Portsmouth, UK","institution_ids":["https://openalex.org/I63072094"]},{"raw_affiliation_string":"The State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9657999873161316,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.8902832269668579},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.774848222732544},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.753038763999939},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.734550416469574},{"id":"https://openalex.org/keywords/iris","display_name":"IRIS (biosensor)","score":0.52217698097229},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.508145809173584},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.49292755126953125},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.47639980912208557},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4497985541820526},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20666459202766418},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1385810375213623}],"concepts":[{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.8902832269668579},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.774848222732544},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753038763999939},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.734550416469574},{"id":"https://openalex.org/C2779503344","wikidata":"https://www.wikidata.org/wiki/Q5973514","display_name":"IRIS (biosensor)","level":3,"score":0.52217698097229},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.508145809173584},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.49292755126953125},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.47639980912208557},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4497985541820526},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20666459202766418},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1385810375213623},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2017.7989194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:researchportal.port.ac.uk:publications/4c61c758-59d5-4a09-b29d-ce6a3c8b9d36","is_oa":true,"landing_page_url":null,"pdf_url":"https://researchportal.port.ac.uk/portal/en/publications/twoeye-modelbased-gaze-estimation-from-a-kinect-sensor(4c61c758-59d5-4a09-b29d-ce6a3c8b9d36).html","source":{"id":"https://openalex.org/S4306401774","display_name":"Portsmouth Research Portal (University of Portsmouth)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63072094","host_organization_name":"University of Portsmouth","host_organization_lineage":["https://openalex.org/I63072094"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:researchportal.port.ac.uk:publications/4c61c758-59d5-4a09-b29d-ce6a3c8b9d36","is_oa":true,"landing_page_url":null,"pdf_url":"https://researchportal.port.ac.uk/portal/en/publications/twoeye-modelbased-gaze-estimation-from-a-kinect-sensor(4c61c758-59d5-4a09-b29d-ce6a3c8b9d36).html","source":{"id":"https://openalex.org/S4306401774","display_name":"Portsmouth Research Portal (University of Portsmouth)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63072094","host_organization_name":"University of Portsmouth","host_organization_lineage":["https://openalex.org/I63072094"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2737029439.pdf"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1970145098","https://openalex.org/W1971652879","https://openalex.org/W1972332041","https://openalex.org/W1972619391","https://openalex.org/W1991942785","https://openalex.org/W2026937303","https://openalex.org/W2043033468","https://openalex.org/W2078841894","https://openalex.org/W2106015463","https://openalex.org/W2112890074","https://openalex.org/W2113165517","https://openalex.org/W2124436197","https://openalex.org/W2136773441","https://openalex.org/W2157285372","https://openalex.org/W2158363907","https://openalex.org/W2167084080","https://openalex.org/W2306767695","https://openalex.org/W2354422866","https://openalex.org/W2397023066","https://openalex.org/W3097096317","https://openalex.org/W6684228839","https://openalex.org/W6705899762","https://openalex.org/W6711844864"],"related_works":["https://openalex.org/W1880689012","https://openalex.org/W3014378845","https://openalex.org/W4240909707","https://openalex.org/W2059546927","https://openalex.org/W1986970529","https://openalex.org/W2562758970","https://openalex.org/W4386072035","https://openalex.org/W2161294397","https://openalex.org/W2012644758","https://openalex.org/W2047559669"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,40,50,68],"present":[4,69],"an":[5,52,142],"effective":[6],"and":[7,32,103],"accurate":[8],"gaze":[9,96,111,127,138],"estimation":[10,47,128,139,144],"method":[11,58,74,82,129,140],"based":[12],"on":[13,130],"two-eye":[14,42],"model":[15,43],"of":[16,22,37,56,110],"a":[17,27,70,88],"subject":[18],"with":[19],"the":[20,35,46,61,84,91,95,100,104,117,125,154],"tolerance":[21],"free":[23],"head":[24],"movement":[25],"from":[26,90],"Kinect":[28],"sensor.":[29],"To":[30],"accurately":[31],"efficiently":[33],"determine":[34,99],"point":[36,97,109],"gaze,":[38],"i)":[39],"employ":[41],"to":[44,59,94,98],"improve":[45],"accuracy;":[48],"ii)":[49],"propose":[51],"improved":[53],"convolution-based":[54],"means":[55],"gradients":[57],"localize":[60],"iris":[62,92],"center":[63,93],"in":[64,153],"3D":[65],"space;":[66],"iii)":[67],"new":[71],"personal":[72,119],"calibration":[73,79],"that":[75,136],"only":[76],"needs":[77],"one":[78],"point.":[80],"The":[81,107],"approximates":[83],"visual":[85],"axis":[86],"as":[87],"line":[89],"eyeball":[101],"centers":[102],"Kappa":[105],"angles.":[106],"final":[108],"can":[112],"be":[113],"calculated":[114],"by":[115],"using":[116],"calibrated":[118],"eye":[120],"parameters.":[121],"We":[122],"experimentally":[123],"evaluate":[124],"proposed":[126],"eleven":[131],"subjects.":[132],"Experimental":[133],"results":[134],"demonstrate":[135],"our":[137],"has":[141],"average":[143],"accuracy":[145],"around":[146],"1.99\u00b0,":[147],"which":[148],"outperforms":[149],"many":[150],"leading":[151],"methods":[152],"state-of-the-art.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}