{"id":"https://openalex.org/W2922188210","doi":"https://doi.org/10.1109/icce.2019.8661836","title":"Automatic Gaze Correction based on Deep Learning and Image Warping","display_name":"Automatic Gaze Correction based on Deep Learning and Image Warping","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2922188210","doi":"https://doi.org/10.1109/icce.2019.8661836","mag":"2922188210"},"language":"en","primary_location":{"id":"doi:10.1109/icce.2019.8661836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce.2019.8661836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5109418409","display_name":"Masataka Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masataka Seo","raw_affiliation_strings":["College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101494292","display_name":"Takahiro Yamamoto","orcid":"https://orcid.org/0000-0003-0017-1152"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahiro Yamamoto","raw_affiliation_strings":["College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044216245","display_name":"Yen\u2010Wei Chen","orcid":"https://orcid.org/0000-0002-5952-0188"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008693267","display_name":"Toshihiro Kitajima","orcid":"https://orcid.org/0000-0002-4193-4796"},"institutions":[{"id":"https://openalex.org/I4210121247","display_name":"Samsung (Japan)","ror":"https://ror.org/01x29j481","country_code":"JP","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210121247"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshihiro Kitajima","raw_affiliation_strings":["ET Center ET-2 Laboratory, Samsung R&D Institute Japan, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ET Center ET-2 Laboratory, Samsung R&D Institute Japan, Osaka, Japan","institution_ids":["https://openalex.org/I4210121247"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.29903442,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9979000091552734,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8614300489425659},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.8072396516799927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8008612394332886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7142048478126526},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.6842293739318848},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.6494259834289551},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6244192123413086},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4401082694530487},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4245149791240692},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4140746593475342}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8614300489425659},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8072396516799927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8008612394332886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7142048478126526},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.6842293739318848},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.6494259834289551},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6244192123413086},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4401082694530487},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4245149791240692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4140746593475342},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce.2019.8661836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce.2019.8661836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1944180106","https://openalex.org/W1947481528","https://openalex.org/W1989583129","https://openalex.org/W2000683961","https://openalex.org/W2038685445","https://openalex.org/W2064675550","https://openalex.org/W2082997166","https://openalex.org/W2115073780","https://openalex.org/W2140447468","https://openalex.org/W2145115810","https://openalex.org/W2152826865","https://openalex.org/W2163605009","https://openalex.org/W2611631463","https://openalex.org/W3147463375","https://openalex.org/W4235748159","https://openalex.org/W6640617836","https://openalex.org/W6684191040","https://openalex.org/W6737263004"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W2735770592","https://openalex.org/W1971024059","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"When":[0],"people":[1],"take":[2],"a":[3,9,59,101,159,168],"selfie":[4],"photo":[5],"or":[6,42,95],"talk":[7],"through":[8],"video":[10,45,105,169],"chat":[11],"system,":[12],"they":[13],"tend":[14],"to":[15,47,61,93,118],"look":[16],"at":[17],"the":[18,21,24,32,34,37,40,54,63,67,77,129,139],"screen.":[19],"Since":[20],"position":[22],"of":[23,31,36,132],"camera":[25],"is":[26,154],"usually":[27],"different":[28],"from":[29],"that":[30],"screen,":[33],"eyes":[35,78],"person":[38],"in":[39,100,143],"photograph":[41],"transmitted":[43],"live":[44],"seem":[46],"make":[48],"contact":[49],"with":[50,138,163],"something":[51],"other":[52],"than":[53],"viewer.":[55],"This":[56,156],"paper":[57],"proposes":[58],"method":[60,157],"correct":[62],"gaze":[64,152,161],"by":[65],"synthesizing":[66],"image":[68,71,103,142],"texture.":[69],"The":[70,108],"synthesis":[72],"uses":[73],"feature":[74,98,111,122,134],"points":[75,99,123,135],"around":[76],"as":[79,126],"landmarks.":[80],"A":[81],"common":[82],"convolutional":[83],"neural":[84],"network":[85,90],"and":[86,104,128,150],"long-term":[87],"recurrent":[88],"convolution":[89],"are":[91,114,124,136],"used":[92,125],"extract":[94],"detect":[96],"these":[97,133,146],"still":[102],"image,":[106],"respectively.":[107],"deep":[109],"learning-based":[110],"point":[112],"detections":[113],"very":[115],"accurate":[116],"compared":[117],"conventional":[119],"methods.":[120],"These":[121],"landmarks,":[127],"internal":[130],"texture":[131],"synthesized":[137],"prepared":[140],"template":[141],"advance.":[144],"Through":[145],"procedures,":[147],"an":[148],"automatic":[149],"natural":[151,160],"correction":[153,162],"realized.":[155],"realized":[158],"little":[164],"blur,":[165],"even":[166],"for":[167],"image.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}