{"id":"https://openalex.org/W1594998705","doi":"https://doi.org/10.1109/fg.2015.7163081","title":"Exploiting sparsity and co-occurrence structure for action unit recognition","display_name":"Exploiting sparsity and co-occurrence structure for action unit recognition","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1594998705","doi":"https://doi.org/10.1109/fg.2015.7163081","mag":"1594998705"},"language":"en","primary_location":{"id":"doi:10.1109/fg.2015.7163081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2015.7163081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)","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/A5100566791","display_name":"Yale Song","orcid":null},"institutions":[{"id":"https://openalex.org/I2800095910","display_name":"Yahoo (Spain)","ror":"https://ror.org/03gq8sg42","country_code":"ES","type":"company","lineage":["https://openalex.org/I2800095910","https://openalex.org/I4210134091"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Yale Song","raw_affiliation_strings":["Yahoo! Research, MIT Media Lab","Yahoo! research,"],"affiliations":[{"raw_affiliation_string":"Yahoo! Research, MIT Media Lab","institution_ids":[]},{"raw_affiliation_string":"Yahoo! research,","institution_ids":["https://openalex.org/I2800095910"]}]},{"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/I4210127225","display_name":"Affectiva (United States)","ror":"https://ror.org/0322cev20","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel McDuff","raw_affiliation_strings":["Affectiva"],"affiliations":[{"raw_affiliation_string":"Affectiva","institution_ids":["https://openalex.org/I4210127225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034738254","display_name":"Deepak Vasisht","orcid":"https://orcid.org/0000-0003-3826-0978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deepak Vasisht","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101933711","display_name":"Ashish Kapoor","orcid":"https://orcid.org/0009-0004-3764-8449"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"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":["GB","US"],"is_corresponding":false,"raw_author_name":"Ashish Kapoor","raw_affiliation_strings":["Microsoft Research","Microsoft research#TAB#"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"Microsoft research#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100566791"],"corresponding_institution_ids":["https://openalex.org/I2800095910"],"apc_list":null,"apc_paid":null,"fwci":9.4918,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.98127696,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9991999864578247,"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":0.9991999864578247,"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/T10860","display_name":"Speech and Audio Processing","score":0.9990000128746033,"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":0.9975000023841858,"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":0.7214707732200623},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6598062515258789},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.6592950224876404},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6060424447059631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5849703550338745},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5846315622329712},{"id":"https://openalex.org/keywords/co-occurrence","display_name":"Co-occurrence","score":0.5323092937469482},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5142073631286621},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5061065554618835},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4629583954811096},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.43400490283966064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38231778144836426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214707732200623},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6598062515258789},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.6592950224876404},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6060424447059631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5849703550338745},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5846315622329712},{"id":"https://openalex.org/C154290570","wikidata":"https://www.wikidata.org/wiki/Q1756768","display_name":"Co-occurrence","level":2,"score":0.5323092937469482},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5142073631286621},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5061065554618835},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4629583954811096},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.43400490283966064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38231778144836426},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fg.2015.7163081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2015.7163081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W414188911","https://openalex.org/W1569534989","https://openalex.org/W1595126664","https://openalex.org/W1970554427","https://openalex.org/W1999894343","https://openalex.org/W2004102643","https://openalex.org/W2045472600","https://openalex.org/W2065379720","https://openalex.org/W2065513175","https://openalex.org/W2077964948","https://openalex.org/W2081749143","https://openalex.org/W2088575594","https://openalex.org/W2101965618","https://openalex.org/W2102570318","https://openalex.org/W2103943262","https://openalex.org/W2104680354","https://openalex.org/W2106390385","https://openalex.org/W2115119296","https://openalex.org/W2115136130","https://openalex.org/W2129106196","https://openalex.org/W2144759647","https://openalex.org/W2145908112","https://openalex.org/W2154683974","https://openalex.org/W2155410841","https://openalex.org/W2156503193","https://openalex.org/W2164129707","https://openalex.org/W2164452299","https://openalex.org/W2299275441","https://openalex.org/W2914746235","https://openalex.org/W4285719527","https://openalex.org/W4290610135","https://openalex.org/W6671151537","https://openalex.org/W6676143902","https://openalex.org/W6681380847","https://openalex.org/W6681897715","https://openalex.org/W6682635902","https://openalex.org/W6683954916","https://openalex.org/W6764172607"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2562263695","https://openalex.org/W2135187896","https://openalex.org/W2147201983","https://openalex.org/W2015518264","https://openalex.org/W2795035211","https://openalex.org/W2160108762","https://openalex.org/W1718066205","https://openalex.org/W2017034551"],"abstract_inverted_index":{"We":[0,75,127],"present":[1],"a":[2,66,77],"novel":[3,78],"Bayesian":[4,79],"framework":[5],"for":[6,69],"facial":[7,26,46,54,89],"action":[8,27,55,90,119],"unit":[9],"recognition.":[10],"The":[11,37],"first":[12],"key":[13],"observation":[14],"behind":[15],"this":[16],"work":[17],"is":[18,39,106],"sparsity:":[19],"out":[20],"of":[21,53,61,73,88,103,109],"possible":[22],"45":[23],"(and":[24],"more)":[25],"units,":[28,56],"only":[29],"very":[30],"few":[31],"are":[32,48],"active":[33],"at":[34],"any":[35],"moment.":[36],"second":[38],"the":[40,59,71,107,155],"strong":[41,67],"statistical":[42],"co-occurrence":[43,139],"structure:":[44],"most":[45],"expressions":[47],"made":[49],"by":[50],"common":[51],"combinations":[52],"so":[57],"knowing":[58],"presence":[60,72],"one":[62],"can":[63,140],"act":[64],"as":[65],"prior":[68],"inferring":[70],"others.":[74],"developed":[76],"graphical":[80],"model":[81],"that":[82,121],"encodes":[83],"these":[84],"two":[85],"natural":[86],"aspects":[87],"units":[91,120],"via":[92],"compressed":[93],"sensing":[94],"and":[95,133,138],"group-wise":[96],"sparsity":[97,137],"inducing":[98],"priors.":[99],"One":[100],"crucial":[101],"aspect":[102],"our":[104],"approach":[105],"allowance":[108],"overlapping":[110],"group":[111],"structures,":[112],"which":[113],"proves":[114],"useful":[115],"in":[116],"dealing":[117],"with":[118],"occur":[122],"frequently":[123],"across":[124],"multiple":[125],"groups.":[126],"derive":[128],"an":[129],"efficient":[130],"inference":[131],"scheme":[132],"show":[134,152],"how":[135],"such":[136],"be":[141],"automatically":[142],"learned":[143],"from":[144],"data.":[145],"Experiments":[146],"on":[147],"three":[148],"standard":[149],"benchmark":[150],"datasets":[151],"superiority":[153],"over":[154],"state-of-the-art.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}