{"id":"https://openalex.org/W3135693821","doi":"https://doi.org/10.1109/acii55700.2022.9953868","title":"DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization","display_name":"DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization","publication_year":2022,"publication_date":"2022-10-18","ids":{"openalex":"https://openalex.org/W3135693821","doi":"https://doi.org/10.1109/acii55700.2022.9953868","mag":"3135693821"},"language":"en","primary_location":{"id":"doi:10.1109/acii55700.2022.9953868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acii55700.2022.9953868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.02484","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022316351","display_name":"Javier Hernandez","orcid":"https://orcid.org/0000-0001-9504-5217"},"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":"Javier Hernandez","raw_affiliation_strings":["Microsoft Research, Microsoft,Redmond,USA","Microsoft Research, Microsoft, Redmond, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Microsoft,Redmond,USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Microsoft, Redmond, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681741","display_name":"Daniel McDuff","orcid":"https://orcid.org/0000-0001-7313-0082"},"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":"Daniel McDuff","raw_affiliation_strings":["Microsoft Research, Microsoft,Redmond,USA","Microsoft Research, Microsoft, Redmond, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Microsoft,Redmond,USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Microsoft, Redmond, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038571306","display_name":"Ognjen Rudovic","orcid":"https://orcid.org/0000-0003-1165-6075"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ognjen Oggi Rudovic","raw_affiliation_strings":["Massachusetts Institute of Technology,Cambridge,USA","Massachusetts Institute of Technology, Cambridge, USA"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Cambridge,USA","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064963095","display_name":"Alberto Fung","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]},{"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":"Alberto Fung","raw_affiliation_strings":["Microsoft Research, Microsoft,Redmond,USA","Microsoft Research, Microsoft, Redmond, USA","University of Houston, Houston, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Microsoft,Redmond,USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Microsoft, Redmond, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"University of Houston, Houston, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020340631","display_name":"Mary Czerwinski","orcid":"https://orcid.org/0000-0003-0881-401X"},"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":"Mary Czerwinski","raw_affiliation_strings":["Microsoft Research, Microsoft,Redmond,USA","Microsoft Research, Microsoft, Redmond, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Microsoft,Redmond,USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Microsoft, Redmond, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5022316351"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":0.4079,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5727096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9994000196456909,"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/T11448","display_name":"Face recognition and analysis","score":0.9994000196456909,"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/T10057","display_name":"Face and Expression Recognition","score":0.9944000244140625,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9900000095367432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7086272239685059},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.692264199256897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6339026093482971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6054409742355347},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.5262284278869629},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.507509171962738},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.502631664276123},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4681340157985687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.419533908367157},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4183984398841858},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3833923935890198},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2254863977432251}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7086272239685059},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.692264199256897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6339026093482971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6054409742355347},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5262284278869629},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.507509171962738},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.502631664276123},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4681340157985687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.419533908367157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4183984398841858},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3833923935890198},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2254863977432251},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","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/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},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/acii55700.2022.9953868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acii55700.2022.9953868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.02484","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.02484","pdf_url":"https://arxiv.org/pdf/2103.02484","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.02484","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.02484","pdf_url":"https://arxiv.org/pdf/2103.02484","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W1595126664","https://openalex.org/W1628791547","https://openalex.org/W2000567862","https://openalex.org/W2003249666","https://openalex.org/W2004068758","https://openalex.org/W2029847666","https://openalex.org/W2045472600","https://openalex.org/W2051297709","https://openalex.org/W2055999471","https://openalex.org/W2062419216","https://openalex.org/W2072372738","https://openalex.org/W2076807218","https://openalex.org/W2090213288","https://openalex.org/W2098615198","https://openalex.org/W2100127573","https://openalex.org/W2104067190","https://openalex.org/W2106390385","https://openalex.org/W2112796928","https://openalex.org/W2117156435","https://openalex.org/W2117866167","https://openalex.org/W2125127226","https://openalex.org/W2129106196","https://openalex.org/W2140312704","https://openalex.org/W2154716422","https://openalex.org/W2159190230","https://openalex.org/W2163928333","https://openalex.org/W2180782754","https://openalex.org/W2326887180","https://openalex.org/W2345305417","https://openalex.org/W2482870886","https://openalex.org/W2607398213","https://openalex.org/W2737559518","https://openalex.org/W2785722081","https://openalex.org/W2788481061","https://openalex.org/W2790831573","https://openalex.org/W2798545058","https://openalex.org/W2799041689","https://openalex.org/W2801211065","https://openalex.org/W2806833697","https://openalex.org/W2807126412","https://openalex.org/W2878701088","https://openalex.org/W2900508683","https://openalex.org/W2903991757","https://openalex.org/W2909336075","https://openalex.org/W2910595231","https://openalex.org/W2911424785","https://openalex.org/W2912336782","https://openalex.org/W2912483755","https://openalex.org/W2915268314","https://openalex.org/W2920684706","https://openalex.org/W2922757351","https://openalex.org/W2943304069","https://openalex.org/W2946287218","https://openalex.org/W2962770929","https://openalex.org/W2962827684","https://openalex.org/W2963495263","https://openalex.org/W2969059826","https://openalex.org/W2982058372","https://openalex.org/W2982340830","https://openalex.org/W2984700035","https://openalex.org/W2990452356","https://openalex.org/W3014897344","https://openalex.org/W3019200173","https://openalex.org/W3031612870","https://openalex.org/W3035170697","https://openalex.org/W3100470991","https://openalex.org/W3102060870","https://openalex.org/W3125532488","https://openalex.org/W3125803510","https://openalex.org/W4250928742","https://openalex.org/W4288404124","https://openalex.org/W4290610135","https://openalex.org/W6681139254","https://openalex.org/W6736728608","https://openalex.org/W6748382702","https://openalex.org/W6759596746","https://openalex.org/W6761140172"],"related_works":["https://openalex.org/W3121380072","https://openalex.org/W2058403539","https://openalex.org/W1508220431","https://openalex.org/W2942793592","https://openalex.org/W2602311653","https://openalex.org/W2333615638","https://openalex.org/W2768231286","https://openalex.org/W2953716828","https://openalex.org/W2904857019","https://openalex.org/W2409976527"],"abstract_inverted_index":{"Deployment":[0],"of":[1,30,66,83,113,127,162],"facial":[2,64,72,165],"action":[3,166],"unit":[4,167],"recognition":[5,168],"models":[6,90,105,129],"has":[7],"been":[8],"impeded":[9],"due":[10],"to":[11,15,62,78,109,137],"their":[12],"limited":[13],"generalization":[14,25,111,141],"unseen":[16],"people":[17,68],"and":[18,37,42,44,47,80,98,150],"demographics.":[19],"This":[20],"work":[21],"conducts":[22],"an":[23,156],"in-depth":[24],"analysis":[26],"across":[27,100],"several":[28],"sources":[29],"variance:":[31],"individuals":[32],"(40":[33],"subjects),":[34],"genders":[35],"(male":[36],"female),":[38],"skin":[39,147],"types":[40],"(darker":[41],"lighter),":[43],"databases":[45],"(BP4D":[46],"DISFA).":[48],"To":[49],"help":[50],"suppress":[51],"the":[52,84,117,120,125,132,140,160],"variance":[53],"in":[54],"data,":[55],"we":[56],"propose":[57],"using":[58],"self-supervised":[59],"denoising":[60],"autoencoders":[61],"transfer":[63],"expressions":[65],"different":[67],"onto":[69],"a":[70,110],"common":[71],"template":[73],"which":[74],"is":[75],"then":[76],"used":[77],"train":[79],"evaluate":[81],"each":[82],"models.":[85],"We":[86],"show":[87],"that":[88],"person-independent":[89,128],"yielded":[91],"significantly":[92,123,138],"lower":[93],"performance":[94,126],"(55%":[95],"average":[96],"F1":[97],"accuracy":[99],"40":[101],"subjects)":[102],"than":[103],"person-dependent":[104],"(60.3":[106],"%),":[107],"leading":[108],"gap":[112,142],"5.3%.":[114],"However,":[115],"normalizing":[116],"data":[118],"with":[119],"proposed":[121,133],"method":[122,134],"increased":[124],"(59.6%).":[130],"Similarly,":[131],"was":[135],"able":[136],"reduce":[139],"when":[143],"considering":[144],"gender":[145],"(2.4%),":[146],"type":[148],"(5.3%),":[149],"dataset":[151],"(9.4%).":[152],"These":[153],"findings":[154],"represent":[155],"important":[157],"step":[158],"towards":[159],"creation":[161],"more":[163],"generalizable":[164],"systems.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-03-15T00:00:00"}
