{"id":"https://openalex.org/W3013897564","doi":"https://doi.org/10.1109/tip.2020.2982828","title":"Gaze Estimation by Exploring Two-Eye Asymmetry","display_name":"Gaze Estimation by Exploring Two-Eye Asymmetry","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3013897564","doi":"https://doi.org/10.1109/tip.2020.2982828","mag":"3013897564","pmid":"https://pubmed.ncbi.nlm.nih.gov/32224460"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.2982828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.2982828","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102735526","display_name":"Yihua Cheng","orcid":"https://orcid.org/0000-0003-1353-9817"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yihua Cheng","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010012679","display_name":"Xucong Zhang","orcid":"https://orcid.org/0000-0002-8368-3542"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Xucong Zhang","raw_affiliation_strings":["Department of Computer Science, ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050527785","display_name":"Feng Lu","orcid":"https://orcid.org/0000-0001-9064-7964"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Lu","raw_affiliation_strings":["Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China","Peng Cheng Laboratory, Shenzhen, China","State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045996641","display_name":"Yoichi Sato","orcid":"https://orcid.org/0000-0003-0097-4537"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoichi Sato","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102735526"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":14.6887,"has_fulltext":false,"cited_by_count":173,"citation_normalized_percentile":{"value":0.99483252,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"29","issue":null,"first_page":"5259","last_page":"5272"},"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.7730255126953125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7244373559951782},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6799117922782898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6514596939086914},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.4909670650959015},{"id":"https://openalex.org/keywords/asymmetry","display_name":"Asymmetry","score":0.4448546767234802}],"concepts":[{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.7730255126953125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7244373559951782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6799117922782898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514596939086914},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.4909670650959015},{"id":"https://openalex.org/C38976095","wikidata":"https://www.wikidata.org/wiki/Q752641","display_name":"Asymmetry","level":2,"score":0.4448546767234802},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2020.2982828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.2982828","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:32224460","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32224460","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3570431576","display_name":null,"funder_award_id":"61972012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":76,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1899110234","https://openalex.org/W1946259682","https://openalex.org/W1979940872","https://openalex.org/W1991544872","https://openalex.org/W1995694455","https://openalex.org/W2001252859","https://openalex.org/W2007593251","https://openalex.org/W2027879843","https://openalex.org/W2034382626","https://openalex.org/W2042906110","https://openalex.org/W2059916030","https://openalex.org/W2077819131","https://openalex.org/W2087806427","https://openalex.org/W2093822429","https://openalex.org/W2097087373","https://openalex.org/W2103289133","https://openalex.org/W2105899846","https://openalex.org/W2107309161","https://openalex.org/W2114902384","https://openalex.org/W2136773441","https://openalex.org/W2150497272","https://openalex.org/W2158363907","https://openalex.org/W2163605009","https://openalex.org/W2167020116","https://openalex.org/W2299591120","https://openalex.org/W2417299834","https://openalex.org/W2468114283","https://openalex.org/W2520274358","https://openalex.org/W2536982654","https://openalex.org/W2557669140","https://openalex.org/W2579934302","https://openalex.org/W2585592883","https://openalex.org/W2598992495","https://openalex.org/W2612135493","https://openalex.org/W2757028014","https://openalex.org/W2758436589","https://openalex.org/W2765629358","https://openalex.org/W2778268008","https://openalex.org/W2790194629","https://openalex.org/W2799064164","https://openalex.org/W2804743778","https://openalex.org/W2883284130","https://openalex.org/W2895535699","https://openalex.org/W2897890508","https://openalex.org/W2921627265","https://openalex.org/W2921653116","https://openalex.org/W2955060956","https://openalex.org/W2963148766","https://openalex.org/W2963334022","https://openalex.org/W2963530216","https://openalex.org/W2964121744","https://openalex.org/W2965638232","https://openalex.org/W2982168673","https://openalex.org/W2982717194","https://openalex.org/W2986953233","https://openalex.org/W2998355285","https://openalex.org/W3090053821","https://openalex.org/W3101708136","https://openalex.org/W3106262690","https://openalex.org/W3122238731","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6640564649","https://openalex.org/W6675405564","https://openalex.org/W6676096000","https://openalex.org/W6749312877","https://openalex.org/W6753184084","https://openalex.org/W6755536180","https://openalex.org/W6755597052","https://openalex.org/W6760079675","https://openalex.org/W6760207242","https://openalex.org/W6765527630","https://openalex.org/W6766282382","https://openalex.org/W6772326284"],"related_works":["https://openalex.org/W2385108104","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747"],"abstract_inverted_index":{"Eye":[0],"gaze":[1,30,70,101,123],"estimation":[2,71],"is":[3,32,38,107],"increasingly":[4],"demanded":[5],"by":[6,45,73,121,179],"recent":[7],"intelligent":[8],"systems":[9],"to":[10,28,40,67,139,150],"facilitate":[11],"a":[12,24],"range":[13],"of":[14,52,147,154,177,183],"interactive":[15],"applications.":[16],"Unfortunately,":[17],"learning":[18,153],"the":[19,29,36,46,53,61,69,75,110,118,126,129,132,145,152,155,175,181],"highly":[20],"complicated":[21],"regression":[22,89],"from":[23],"single":[25],"eye":[26],"image":[27],"direction":[31],"not":[33],"trivial.":[34],"Thus,":[35],"problem":[37],"yet":[39],"be":[41],"solved":[42],"efficiently.":[43],"Inspired":[44],"two-eye":[47,122],"asymmetry":[48],"as":[49],"two":[50,148],"eyes":[51,105,133,149],"same":[54],"person":[55],"may":[56],"appear":[57],"uneven,":[58],"we":[59,173],"propose":[60],"face-based":[62,87],"asymmetric":[63,88,111,127,156],"regression-evaluation":[64],"network":[65,90,95],"(FARE-Net)":[66],"optimize":[68,140],"results":[72],"considering":[74],"difference":[76],"between":[77],"left":[78],"and":[79,92,106,116,158,169,185],"right":[80],"eyes.":[81],"The":[82,97,142],"proposed":[83],"method":[84],"includes":[85],"one":[86,93],"(FAR-Net)":[91],"evaluation":[94],"(E-Net).":[96],"FAR-Net":[98,130],"predicts":[99],"3D":[100],"directions":[102],"for":[103],"both":[104],"trained":[108],"with":[109],"mechanism,":[112,128],"which":[113],"asymmetrically":[114],"weights":[115],"sums":[117],"loss":[119],"generated":[120],"directions.":[124],"With":[125],"utilizes":[131],"that":[134],"can":[135],"achieve":[136],"high":[137],"performance":[138],"network.":[141],"E-Net":[143],"learns":[144],"reliabilities":[146],"balance":[151],"mechanism":[157],"symmetric":[159],"mechanism.":[160],"Our":[161],"FARENet":[162],"achieves":[163],"leading":[164],"performances":[165],"on":[166],"MPIIGaze,":[167],"EyeDiap":[168],"RT-Gene":[170],"datasets.":[171],"Additionally,":[172],"investigate":[174],"effectiveness":[176],"FARE-Net":[178],"analyzing":[180],"distribution":[182],"errors":[184],"ablation":[186],"study.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":47},{"year":2023,"cited_by_count":37},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":5}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}