{"id":"https://openalex.org/W2922469850","doi":"https://doi.org/10.1109/fg.2019.8756523","title":"LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation","display_name":"LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2922469850","doi":"https://doi.org/10.1109/fg.2019.8756523","mag":"2922469850"},"language":"en","primary_location":{"id":"doi:10.1109/fg.2019.8756523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2019.8756523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th IEEE International Conference on Automatic Face &amp; Gesture Recognition (FG 2019)","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/A5083736757","display_name":"Reo Ogusu","orcid":null},"institutions":[{"id":"https://openalex.org/I42999171","display_name":"Sophia University","ror":"https://ror.org/01nckkm68","country_code":"JP","type":"education","lineage":["https://openalex.org/I42999171"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Reo Ogusu","raw_affiliation_strings":["Department of Information and Communication Sciences, Sophia University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Sciences, Sophia University, Tokyo, Japan","institution_ids":["https://openalex.org/I42999171"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101242929","display_name":"Takao Yamanaka","orcid":"https://orcid.org/0000-0001-9028-8244"},"institutions":[{"id":"https://openalex.org/I42999171","display_name":"Sophia University","ror":"https://ror.org/01nckkm68","country_code":"JP","type":"education","lineage":["https://openalex.org/I42999171"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takao Yamanaka","raw_affiliation_strings":["Department of Information and Communication Sciences, Sophia University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Sciences, Sophia University, Tokyo, Japan","institution_ids":["https://openalex.org/I42999171"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I42999171"],"apc_list":null,"apc_paid":null,"fwci":0.558,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.55773177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9902999997138977,"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"}},{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.8190882205963135},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7769081592559814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7634181380271912},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7387561202049255},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6411736011505127},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.5734673142433167},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5608359575271606},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43529239296913147},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41273146867752075},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33232438564300537}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.8190882205963135},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7769081592559814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7634181380271912},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7387561202049255},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6411736011505127},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.5734673142433167},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5608359575271606},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43529239296913147},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41273146867752075},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33232438564300537},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fg.2019.8756523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2019.8756523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th IEEE International Conference on Automatic Face &amp; Gesture Recognition (FG 2019)","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":28,"referenced_works":["https://openalex.org/W1502024436","https://openalex.org/W1519033008","https://openalex.org/W1536680647","https://openalex.org/W1995694455","https://openalex.org/W2014467472","https://openalex.org/W2027879843","https://openalex.org/W2042906110","https://openalex.org/W2163605009","https://openalex.org/W2166266461","https://openalex.org/W2274218579","https://openalex.org/W2279098554","https://openalex.org/W2407521645","https://openalex.org/W2408244139","https://openalex.org/W2468114283","https://openalex.org/W2557669140","https://openalex.org/W2607679123","https://openalex.org/W2758436589","https://openalex.org/W2786321467","https://openalex.org/W2903328275","https://openalex.org/W2950800384","https://openalex.org/W2952055246","https://openalex.org/W3106262690","https://openalex.org/W6684191040","https://openalex.org/W6684424169","https://openalex.org/W6694261494","https://openalex.org/W6713983332","https://openalex.org/W6714138976","https://openalex.org/W6981618586"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W2810679507"],"abstract_inverted_index":{"Gaze":[0],"tracking":[1,23],"is":[2,71],"an":[3],"important":[4],"technology":[5],"in":[6,118],"many":[7],"domains.":[8],"Techniques":[9],"such":[10,31],"as":[11,32],"Convolutional":[12],"Neural":[13],"Networks":[14],"(CNNs)":[15],"have":[16],"allowed":[17],"the":[18,21,33,44,56,68,72,77,89,113,116,119,127,140,149],"invention":[19],"of":[20,88,115,129],"gaze":[22,48,141],"method":[24,138],"that":[25,43],"relies":[26],"only":[27],"on":[28,35],"commodity":[29],"hardware":[30],"camera":[34],"a":[36,52,64,98,145,154],"personal":[37],"computer.":[38],"It":[39],"has":[40],"been":[41],"shown":[42],"full-face":[45,69,91],"region":[46],"for":[47],"estimation":[49,142],"can":[50,105],"provide":[51],"better":[53],"performance":[54],"than":[55],"one":[57],"obtained":[58],"from":[59],"eye":[60],"image":[61,70,79,92,150],"alone.":[62],"However,":[63],"problem":[65,85],"with":[66],"using":[67,97],"heavy":[73],"computation":[74],"due":[75],"to":[76,111,153],"larger":[78],"size.":[80,156],"This":[81,136],"study":[82],"tackles":[83],"this":[84],"through":[86],"compression":[87],"input":[90],"by":[93,109],"removing":[94],"redundant":[95],"information":[96],"novel":[99],"learnable":[100,123],"pooling":[101,120,124],"module.":[102],"The":[103,122],"module":[104,125],"be":[106],"trained":[107],"end-to-end":[108],"backpropagation":[110],"learn":[112],"size":[114],"grid":[117],"filter.":[121],"keeps":[126],"resolution":[128],"valuable":[130],"regions":[131],"high":[132],"and":[133],"vice":[134],"versa.":[135],"proposed":[137],"preserved":[139],"accuracy":[143],"at":[144],"certain":[146],"level":[147],"when":[148],"was":[151],"reduced":[152],"smaller":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
