{"id":"https://openalex.org/W2407874045","doi":"https://doi.org/10.1109/wacv.2016.7477675","title":"A practical approach to real-time neutral feature subtraction for facial expression recognition","display_name":"A practical approach to real-time neutral feature subtraction for facial expression recognition","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2407874045","doi":"https://doi.org/10.1109/wacv.2016.7477675","mag":"2407874045"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2016.7477675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477675","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5069105490","display_name":"Nick Haber","orcid":"https://orcid.org/0000-0001-8804-7804"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nick Haber","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018761586","display_name":"Catalin Voss","orcid":"https://orcid.org/0000-0001-6480-7020"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Catalin Voss","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056114450","display_name":"Azar Fazel","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Azar Fazel","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005685978","display_name":"Terry Winograd","orcid":"https://orcid.org/0000-0003-4435-7215"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Terry Winograd","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027872863","display_name":"Dennis P. Wall","orcid":"https://orcid.org/0000-0002-7889-9146"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dennis P. Wall","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0282,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.91098934,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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/T11448","display_name":"Face recognition and analysis","score":0.9983000159263611,"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":0.9958000183105469,"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/computer-science","display_name":"Computer science","score":0.7053977251052856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6991926431655884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6303061246871948},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6088804602622986},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.531012237071991},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5261642336845398},{"id":"https://openalex.org/keywords/subtraction","display_name":"Subtraction","score":0.5215963125228882},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5104314684867859},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.5018484592437744},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47180238366127014},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.4455026686191559},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.420604944229126},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3536228537559509},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20829179883003235},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.1465696096420288}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053977251052856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6991926431655884},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6303061246871948},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6088804602622986},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.531012237071991},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5261642336845398},{"id":"https://openalex.org/C68060419","wikidata":"https://www.wikidata.org/wiki/Q40754","display_name":"Subtraction","level":2,"score":0.5215963125228882},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5104314684867859},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.5018484592437744},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47180238366127014},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.4455026686191559},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.420604944229126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3536228537559509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20829179883003235},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.1465696096420288},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2016.7477675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477675","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","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":45,"referenced_works":["https://openalex.org/W123476658","https://openalex.org/W158871195","https://openalex.org/W1483662103","https://openalex.org/W1551909886","https://openalex.org/W1566413196","https://openalex.org/W1569534989","https://openalex.org/W1623931752","https://openalex.org/W1996218183","https://openalex.org/W2008635359","https://openalex.org/W2029847666","https://openalex.org/W2034714139","https://openalex.org/W2053432263","https://openalex.org/W2060515346","https://openalex.org/W2075688085","https://openalex.org/W2090213288","https://openalex.org/W2097128017","https://openalex.org/W2097290407","https://openalex.org/W2103943262","https://openalex.org/W2105934661","https://openalex.org/W2120100419","https://openalex.org/W2124386111","https://openalex.org/W2131081720","https://openalex.org/W2139916508","https://openalex.org/W2151043394","https://openalex.org/W2154412566","https://openalex.org/W2156516654","https://openalex.org/W2156848952","https://openalex.org/W2157285372","https://openalex.org/W2158198839","https://openalex.org/W2161969291","https://openalex.org/W2162418306","https://openalex.org/W2296659146","https://openalex.org/W2402955625","https://openalex.org/W2542323081","https://openalex.org/W4210993929","https://openalex.org/W4211153864","https://openalex.org/W4211232216","https://openalex.org/W4285719527","https://openalex.org/W4381059462","https://openalex.org/W6604957493","https://openalex.org/W6636380590","https://openalex.org/W6659090348","https://openalex.org/W6682887448","https://openalex.org/W6683411478","https://openalex.org/W6697219101"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2188430267","https://openalex.org/W2058768573","https://openalex.org/W2988126442","https://openalex.org/W2610698896","https://openalex.org/W2369265144","https://openalex.org/W2059865486"],"abstract_inverted_index":{"Methods":[0],"for":[1,70,93,188],"automated":[2],"facial":[3,133],"expression":[4,39],"recognition":[5],"-":[6,14,90],"identifying":[7],"faces":[8],"as":[9,169,183,271],"happy,":[10],"sad,":[11],"angry,":[12],"etc.":[13],"typically":[15],"rely":[16],"on":[17,35,221,235,243],"the":[18,38,46,54,58,112,142,236,253],"classification":[19,96,245],"of":[20,53,60,97,160,163,214,252],"features":[21,151],"extracted":[22],"from":[23,154],"images.":[24],"These":[25],"features,":[26],"designed":[27],"to":[28,66,105,118,123,141,144,150,177,186,203,227],"encode":[29],"shape":[30],"and":[31,44,50,73,190,206,277],"texture":[32],"information,":[33],"depend":[34],"both":[36,275],"(1)":[37],"an":[40,71,170],"individual":[41,72,143,171],"is":[42,65,87,201],"making,":[43],"(2)":[45],"individual's":[47,77,113],"physical":[48],"characteristics":[49],"lighting":[51,191],"conditions":[52],"image.":[55],"To":[56],"reduce":[57],"effect":[59],"(2),":[61],"a":[62,68,98,136,161,184,196,211,259],"common":[63],"strategy":[64],"establish":[67],"\"baseline\"":[69],"subtract":[74],"out":[75],"this":[76,120,219],"baseline":[78,229],"neutral":[79,83,114,175,232],"feature.":[80,115],"This":[81],"extra":[82],"feature":[84,181,215,222,262],"information":[85],"often":[86],"not":[88],"available":[89],"in":[91,103,207],"particular":[92],"in-the-wild,":[94],"real-time":[95,198],"previously":[99],"unseen":[100],"subject.":[101],"Thus,":[102],"order":[104],"implement":[106],"\"neutral":[107],"subtraction,\"":[108],"one":[109],"must":[110],"estimate":[111],"Existing":[116],"methods":[117],"do":[119],"are":[121,148],"susceptible":[122],"class":[124,204,213],"imbalance":[125,205],"at":[126],"test":[127,218],"time":[128],"(e.g.,":[129],"averaging":[130],"over":[131,210],"all":[132],"features),":[134],"require":[135],"more":[137],"complex":[138],"model":[139],"specific":[140],"be":[145],"trained,":[146],"or":[147],"restricted":[149],"computed":[152],"entirely":[153],"tracked":[155],"landmark":[156],"points":[157],"(taking":[158],"advantage":[159],"subset":[162],"\"stable":[164],"points\"":[165],"which":[166,200,264],"move":[167],"little":[168],"emotes).":[172],"We":[173,193,217,240],"extend":[174],"subtraction":[176,233],"different":[178],"computer":[179],"vision":[180],"spaces":[182],"method":[185,199,220,248],"correct":[187],"inter-face":[189],"variance.":[192],"further":[194],"propose":[195],"simple,":[197],"robust":[202],"principal":[208],"works":[209],"wide":[212],"choices.":[216],"extraction":[223],"techniques":[224],"that":[225,242,267],"lead":[226],"high":[228],"accuracy":[230],"without":[231],"(97%":[234],"Extended":[237],"Cohn-Kanade":[238],"Dataset).":[239],"find":[241],"difficult":[244],"tasks":[246],"our":[247],"recovers":[249],"almost":[250],"2/3":[251],"~":[254],"8%":[255],"gain":[256],"shown":[257],"by":[258],"\"cheating\"":[260],"neutral-subtracted":[261],"classifier,":[263],"uses":[265],"examples":[266],"have":[268],"been":[269],"labeled":[270],"neutral,":[272],"validating":[273],"with":[274],"HOG":[276],"SIFT":[278],"features.":[279]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
