{"id":"https://openalex.org/W2293804193","doi":"https://doi.org/10.1145/2818346.2830585","title":"Capturing AU-Aware Facial Features and Their Latent Relations for Emotion Recognition in the Wild","display_name":"Capturing AU-Aware Facial Features and Their Latent Relations for Emotion Recognition in the Wild","publication_year":2015,"publication_date":"2015-11-09","ids":{"openalex":"https://openalex.org/W2293804193","doi":"https://doi.org/10.1145/2818346.2830585","mag":"2293804193"},"language":"en","primary_location":{"id":"doi:10.1145/2818346.2830585","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2818346.2830585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"article","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/A5025017745","display_name":"Anbang Yao","orcid":"https://orcid.org/0000-0002-3878-8679"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Anbang Yao","raw_affiliation_strings":["Intel Labs China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Intel Labs China, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030902617","display_name":"Junchao Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junchao Shao","raw_affiliation_strings":["Intel Labs China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Intel Labs China, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008472627","display_name":"Ningning Ma","orcid":"https://orcid.org/0000-0003-4628-8831"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningning Ma","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100652865","display_name":"Yurong Chen","orcid":"https://orcid.org/0000-0001-9333-1746"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yurong Chen","raw_affiliation_strings":["Intel Labs China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Intel Labs China, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025017745"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.816,"has_fulltext":false,"cited_by_count":96,"citation_normalized_percentile":{"value":0.99401509,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"451","last_page":"458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9969000220298767,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9919000267982483,"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/computer-science","display_name":"Computer science","score":0.7013795971870422},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6289094686508179},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.6039144992828369},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5451353192329407},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5369579195976257},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48505061864852905},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46004417538642883},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4424845278263092},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4274037182331085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013795971870422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6289094686508179},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.6039144992828369},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5451353192329407},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5369579195976257},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48505061864852905},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46004417538642883},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4424845278263092},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4274037182331085},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2818346.2830585","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2818346.2830585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1916406603","https://openalex.org/W1964920275","https://openalex.org/W1976066595","https://openalex.org/W1981918162","https://openalex.org/W1984354005","https://openalex.org/W1992227055","https://openalex.org/W2000820603","https://openalex.org/W2008887256","https://openalex.org/W2081835714","https://openalex.org/W2088575594","https://openalex.org/W2098951409","https://openalex.org/W2102570318","https://openalex.org/W2103943262","https://openalex.org/W2118789253","https://openalex.org/W2125127226","https://openalex.org/W2129106196","https://openalex.org/W2138406903","https://openalex.org/W2139916508","https://openalex.org/W2151103935","https://openalex.org/W2152826865","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2156503193","https://openalex.org/W2157285372","https://openalex.org/W2160815625","https://openalex.org/W2161634108","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2164598857","https://openalex.org/W2174772062","https://openalex.org/W2243226955","https://openalex.org/W2995034616","https://openalex.org/W4285719527","https://openalex.org/W6648088351","https://openalex.org/W6677767046","https://openalex.org/W6683817158"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2811106690","https://openalex.org/W2590798552","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W4210874298"],"abstract_inverted_index":{"The":[0],"Emotion":[1],"Recognition":[2],"in":[3,71,113,140],"the":[4,38,41,61,65,117,125,187,192,195,212,225,234,245,249,257],"Wild":[5],"(EmotiW)":[6],"Challenge":[7,190],"has":[8,77],"been":[9],"held":[10],"for":[11,100],"three":[12,78],"years.":[13],"Previous":[14],"winner":[15,251],"teams":[16],"primarily":[17],"focus":[18],"on":[19,186,208],"designing":[20],"specific":[21],"deep":[22,30],"neural":[23],"networks":[24],"or":[25],"fusing":[26],"diverse":[27],"hand-crafted":[28],"and":[29,233,248,261],"convolutional":[31],"features.":[32,138],"They":[33],"all":[34,177],"neglect":[35],"to":[36,88,158],"explore":[37],"significance":[39],"of":[40,63,93,119,131,194,231,259],"latent":[42],"relations":[43,66,161,182],"among":[44,67],"changing":[45],"features":[46,70,126],"resulted":[47],"from":[48,60,128],"facial":[49,69,94,132,137,153,166,180,216],"muscle":[50],"motions.":[51],"In":[52],"this":[53,57],"paper,":[54],"we":[55,82,144],"study":[56],"recognition":[58,218,222,238,242],"challenge":[59],"perspective":[62],"analyzing":[64],"expression-specific":[68,120],"an":[72,146],"explicit":[73],"manner.":[74],"Our":[75],"method":[76],"key":[79],"components.":[80],"First,":[81],"propose":[83],"a":[84,91,169,229],"pair-wise":[85,142],"learning":[86],"strategy":[87],"automatically":[89],"seek":[90],"set":[92],"image":[95,213],"patches":[96,111,133,154],"which":[97,150],"are":[98,112,134],"important":[99],"discriminating":[101],"two":[102,164],"particular":[103],"emotion":[104,171,237],"categories.":[105],"We":[106],"found":[107],"these":[108],"learnt":[109,152,165],"local":[110],"part":[114],"consistent":[115],"with":[116,228,256],"locations":[118],"Action":[121],"Units":[122],"(AUs),":[123],"thus":[124],"extracted":[127],"such":[129],"kind":[130],"named":[135],"AU-aware":[136],"Second,":[139],"each":[141],"task,":[143],"use":[145],"undirected":[147],"graph":[148],"structure,":[149],"takes":[151],"as":[155],"individual":[156],"vertices,":[157],"encode":[159],"feature":[160,181],"between":[162],"any":[163],"patches.":[167],"Finally,":[168],"robust":[170],"representation":[172],"is":[173],"constructed":[174],"by":[175],"concatenating":[176],"task-specific":[178],"graph-structured":[179],"sequentially.":[183],"Extensive":[184],"experiments":[185],"EmotiW":[188],"2015":[189],"testify":[191],"efficacy":[193],"proposed":[196],"approach.":[197],"Without":[198],"using":[199],"additional":[200],"data,":[201],"our":[202],"final":[203,253],"submissions":[204],"achieved":[205],"competitive":[206],"results":[207],"both":[209],"sub-challenges":[210],"including":[211],"based":[214,236],"static":[215],"expression":[217],"(we":[219,239],"got":[220,240],"55.38%":[221],"accuracy":[223,243],"outperforming":[224,244],"baseline":[226,246],"39.13%":[227],"margin":[230],"16.25%)":[232],"audio-video":[235],"53.80%":[241],"39.33%":[247],"2014":[250],"team's":[252],"result":[254],"50.37%":[255],"margins":[258],"14.47%":[260],"3.43%,":[262],"respectively).":[263]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":27},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":16}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}