{"id":"https://openalex.org/W2481681431","doi":"https://doi.org/10.1145/2993148.2993165","title":"Training deep networks for facial expression recognition with crowd-sourced label distribution","display_name":"Training deep networks for facial expression recognition with crowd-sourced label distribution","publication_year":2016,"publication_date":"2016-10-31","ids":{"openalex":"https://openalex.org/W2481681431","doi":"https://doi.org/10.1145/2993148.2993165","mag":"2481681431"},"language":"en","primary_location":{"id":"doi:10.1145/2993148.2993165","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2993148.2993165","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"preprint","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/A5027115167","display_name":"Emad Barsoum","orcid":"https://orcid.org/0000-0002-4097-8690"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Emad Barsoum","raw_affiliation_strings":["Microsoft Research, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103551927","display_name":"Cha Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cha Zhang","raw_affiliation_strings":["Microsoft Research, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079611849","display_name":"Cristian Canton Ferrer","orcid":"https://orcid.org/0000-0002-3189-5498"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cristian Canton Ferrer","raw_affiliation_strings":["Microsoft Research, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058849717","display_name":"Zhengyou Zhang","orcid":"https://orcid.org/0000-0002-6606-2525"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengyou Zhang","raw_affiliation_strings":["Microsoft Research, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027115167"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":25.5201,"has_fulltext":false,"cited_by_count":859,"citation_normalized_percentile":{"value":0.9975368,"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":"279","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9915000200271606,"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/T10860","display_name":"Speech and Audio Processing","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7839975357055664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.734843909740448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6694536805152893},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6662493348121643},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.6413404941558838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5362775921821594},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.5047107934951782},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49603161215782166},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.4638192057609558},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4559105634689331},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4454590976238251},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.43280139565467834},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.43212002515792847},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4309406876564026},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42489922046661377},{"id":"https://openalex.org/keywords/facial-expression-recognition","display_name":"Facial expression recognition","score":0.4122489094734192},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07916036248207092}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839975357055664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.734843909740448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6694536805152893},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6662493348121643},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.6413404941558838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5362775921821594},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5047107934951782},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49603161215782166},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.4638192057609558},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4559105634689331},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4454590976238251},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.43280139565467834},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.43212002515792847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4309406876564026},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42489922046661377},{"id":"https://openalex.org/C2987714656","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Facial expression recognition","level":4,"score":0.4122489094734192},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07916036248207092},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2993148.2993165","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2993148.2993165","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6600000262260437,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W156959604","https://openalex.org/W1588539311","https://openalex.org/W1607979445","https://openalex.org/W1686810756","https://openalex.org/W1970381522","https://openalex.org/W1974210421","https://openalex.org/W1975798038","https://openalex.org/W2003238582","https://openalex.org/W2014713986","https://openalex.org/W2024166834","https://openalex.org/W2030931454","https://openalex.org/W2058787788","https://openalex.org/W2066525982","https://openalex.org/W2081835714","https://openalex.org/W2097117768","https://openalex.org/W2106390385","https://openalex.org/W2139916508","https://openalex.org/W2151008812","https://openalex.org/W2151401338","https://openalex.org/W2154683974","https://openalex.org/W2162426832","https://openalex.org/W2163605009","https://openalex.org/W2165766797","https://openalex.org/W2170876097","https://openalex.org/W2194775991","https://openalex.org/W2198512331","https://openalex.org/W2246249023","https://openalex.org/W2523478734","https://openalex.org/W2525412388","https://openalex.org/W2604272474","https://openalex.org/W2618530766","https://openalex.org/W2747301830","https://openalex.org/W2949833607","https://openalex.org/W4388365440","https://openalex.org/W6635087628","https://openalex.org/W6651146637","https://openalex.org/W6685269245"],"related_works":["https://openalex.org/W4205986151","https://openalex.org/W2355913164","https://openalex.org/W2162992774","https://openalex.org/W1153638794","https://openalex.org/W2168968280","https://openalex.org/W2901126000","https://openalex.org/W4323520705","https://openalex.org/W2116055069","https://openalex.org/W2356663679","https://openalex.org/W3027190010"],"abstract_inverted_index":{"Crowd":[0],"sourcing":[1],"has":[2],"become":[3],"a":[4,16,34],"widely":[5],"adopted":[6],"scheme":[7,88],"to":[8,32,56,66],"collect":[9],"ground":[10],"truth":[11],"labels.":[12],"However,":[13],"it":[14],"is":[15],"well-known":[17],"problem":[18],"that":[19,83,99],"these":[20],"labels":[21,112],"can":[22],"be":[23,119],"very":[24],"noisy.":[25],"In":[26],"this":[27],"paper,":[28],"we":[29,52],"demonstrate":[30],"how":[31],"learn":[33],"deep":[35],"convolutional":[36],"neural":[37],"network":[38],"(DCNN)":[39],"from":[40],"noisy":[41],"labels,":[42],"using":[43],"facial":[44],"expression":[45],"recognition":[46],"as":[47,92,94],"an":[48],"example.":[49],"More":[50],"specifically,":[51],"have":[53],"10":[54],"taggers":[55],"label":[57,76,103],"each":[58,114],"input":[59],"image,":[60],"and":[61,78],"compare":[62],"four":[63],"different":[64],"approaches":[65,98],"utilizing":[67],"the":[68,84,95,102,122],"multiple":[69,111],"labels:":[70],"majority":[71,86],"voting,":[72],"multi-label":[73],"learning,":[74],"probabilistic":[75],"drawing,":[77],"cross-entropy":[79],"loss.":[80],"We":[81],"show":[82],"traditional":[85],"voting":[87],"does":[89],"not":[90],"perform":[91],"well":[93],"last":[96],"two":[97],"fully":[100],"leverage":[101],"distribution.":[104],"An":[105],"enhanced":[106],"FER+":[107],"data":[108],"set":[109],"with":[110,121],"for":[113],"face":[115],"image":[116],"will":[117],"also":[118],"shared":[120],"research":[123],"community.":[124]},"counts_by_year":[{"year":2026,"cited_by_count":26},{"year":2025,"cited_by_count":145},{"year":2024,"cited_by_count":151},{"year":2023,"cited_by_count":155},{"year":2022,"cited_by_count":100},{"year":2021,"cited_by_count":107},{"year":2020,"cited_by_count":75},{"year":2019,"cited_by_count":58},{"year":2018,"cited_by_count":30},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":2}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}