{"id":"https://openalex.org/W3003759162","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207250","title":"Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor","display_name":"Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3003759162","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207250","mag":"3003759162"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2001.11976","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080559822","display_name":"Sevegni Odilon Clement Allognon","orcid":null},"institutions":[{"id":"https://openalex.org/I159129438","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al","ror":"https://ror.org/002rjbv21","country_code":"CA","type":"education","lineage":["https://openalex.org/I159129438","https://openalex.org/I49663120"]},{"id":"https://openalex.org/I9736820","display_name":"\u00c9cole de Technologie Sup\u00e9rieure","ror":"https://ror.org/0020snb74","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I9736820"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Sevegni Odilon Clement Allognon","raw_affiliation_strings":["\u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC, Canada","Ecole de technologie sup\u00e9rieure"],"affiliations":[{"raw_affiliation_string":"\u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC, Canada","institution_ids":["https://openalex.org/I9736820","https://openalex.org/I159129438"]},{"raw_affiliation_string":"Ecole de technologie sup\u00e9rieure","institution_ids":["https://openalex.org/I9736820"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017591391","display_name":"Alceu de Souza Britto","orcid":"https://orcid.org/0000-0002-3064-3563"},"institutions":[{"id":"https://openalex.org/I176838256","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1","ror":"https://ror.org/02x1vjk79","country_code":"BR","type":"education","lineage":["https://openalex.org/I176838256"]},{"id":"https://openalex.org/I9736820","display_name":"\u00c9cole de Technologie Sup\u00e9rieure","ror":"https://ror.org/0020snb74","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I9736820"]}],"countries":["BR","CA"],"is_corresponding":false,"raw_author_name":"Alceu de S. Britto","raw_affiliation_strings":["Pontifical Catholic University of Paran\u00e1, Curitiba, PR, Brazil","Ecole de technologie sup\u00e9rieure"],"affiliations":[{"raw_affiliation_string":"Pontifical Catholic University of Paran\u00e1, Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I176838256"]},{"raw_affiliation_string":"Ecole de technologie sup\u00e9rieure","institution_ids":["https://openalex.org/I9736820"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074262951","display_name":"Alessandro L. Koerich","orcid":"https://orcid.org/0000-0001-5879-7014"},"institutions":[{"id":"https://openalex.org/I159129438","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al","ror":"https://ror.org/002rjbv21","country_code":"CA","type":"education","lineage":["https://openalex.org/I159129438","https://openalex.org/I49663120"]},{"id":"https://openalex.org/I176838256","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1","ror":"https://ror.org/02x1vjk79","country_code":"BR","type":"education","lineage":["https://openalex.org/I176838256"]},{"id":"https://openalex.org/I9736820","display_name":"\u00c9cole de Technologie Sup\u00e9rieure","ror":"https://ror.org/0020snb74","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I9736820"]}],"countries":["BR","CA"],"is_corresponding":false,"raw_author_name":"Alessandro L. Koerich","raw_affiliation_strings":["\u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC, Canada","Pontif\u00edcal Catholic University of Parana"],"affiliations":[{"raw_affiliation_string":"\u00c9cole de Technologie Sup\u00e9rieure, Universit\u00e9 du Qu\u00e9bec, Montr\u00e9al, QC, Canada","institution_ids":["https://openalex.org/I9736820","https://openalex.org/I159129438"]},{"raw_affiliation_string":"Pontif\u00edcal Catholic University of Parana","institution_ids":["https://openalex.org/I176838256"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080559822"],"corresponding_institution_ids":["https://openalex.org/I159129438","https://openalex.org/I9736820"],"apc_list":null,"apc_paid":null,"fwci":0.3563,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63729952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10057","display_name":"Face and Expression Recognition","score":0.9930999875068665,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.828761875629425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7285265326499939},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7267476320266724},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.6533284187316895},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.6300667524337769},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.6166908144950867},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5790092349052429},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5308985114097595},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.525111734867096},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5210732221603394},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5172094106674194},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4768315851688385},{"id":"https://openalex.org/keywords/concordance-correlation-coefficient","display_name":"Concordance correlation coefficient","score":0.46566295623779297},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.43975910544395447},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4353767931461334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3898196220397949},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13397368788719177}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.828761875629425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7285265326499939},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7267476320266724},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.6533284187316895},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.6300667524337769},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.6166908144950867},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5790092349052429},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5308985114097595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.525111734867096},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5210732221603394},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5172094106674194},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4768315851688385},{"id":"https://openalex.org/C2781059462","wikidata":"https://www.wikidata.org/wiki/Q5158906","display_name":"Concordance correlation coefficient","level":2,"score":0.46566295623779297},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.43975910544395447},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4353767931461334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3898196220397949},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13397368788719177},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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":4,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2001.11976","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.11976","pdf_url":"https://arxiv.org/pdf/2001.11976","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"},{"id":"mag:3003759162","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2001.11976.pdf","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2001.11976","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2001.11976","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2001.11976","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.11976","pdf_url":"https://arxiv.org/pdf/2001.11976","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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3003759162.pdf","grobid_xml":"https://content.openalex.org/works/W3003759162.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1546411676","https://openalex.org/W1785074626","https://openalex.org/W1974210421","https://openalex.org/W1992280180","https://openalex.org/W2008887256","https://openalex.org/W2012485643","https://openalex.org/W2021890937","https://openalex.org/W2025427990","https://openalex.org/W2037441721","https://openalex.org/W2045528981","https://openalex.org/W2051396567","https://openalex.org/W2066332159","https://openalex.org/W2072803611","https://openalex.org/W2081835714","https://openalex.org/W2102570318","https://openalex.org/W2109774206","https://openalex.org/W2112120662","https://openalex.org/W2119217515","https://openalex.org/W2139916508","https://openalex.org/W2141990597","https://openalex.org/W2145310492","https://openalex.org/W2150137812","https://openalex.org/W2157653492","https://openalex.org/W2161362162","https://openalex.org/W2162280462","https://openalex.org/W2164186291","https://openalex.org/W2165183780","https://openalex.org/W2167015621","https://openalex.org/W2169166781","https://openalex.org/W2198512331","https://openalex.org/W2244142460","https://openalex.org/W2246249023","https://openalex.org/W2313339984","https://openalex.org/W2346454595","https://openalex.org/W2531466839","https://openalex.org/W2560025328","https://openalex.org/W2610961739","https://openalex.org/W2913721463","https://openalex.org/W2991485489","https://openalex.org/W6632670727","https://openalex.org/W6670991099","https://openalex.org/W6684580761","https://openalex.org/W6687716273","https://openalex.org/W6689468731","https://openalex.org/W6729112190"],"related_works":["https://openalex.org/W3091389496","https://openalex.org/W3188183700","https://openalex.org/W3084140714","https://openalex.org/W3159409470","https://openalex.org/W3205068595","https://openalex.org/W3130875629","https://openalex.org/W3043400672","https://openalex.org/W2767629878","https://openalex.org/W3127502324","https://openalex.org/W2963999921","https://openalex.org/W2965111981","https://openalex.org/W2798285655","https://openalex.org/W2343758848","https://openalex.org/W3211305627","https://openalex.org/W2963464104","https://openalex.org/W3213879871","https://openalex.org/W2778993047","https://openalex.org/W3160016904","https://openalex.org/W2009059481","https://openalex.org/W2176865601"],"abstract_inverted_index":{"Automatic":[0],"facial":[1],"expression":[2],"recognition":[3,13,86],"(FER)":[4],"is":[5,41],"an":[6,92],"important":[7],"research":[8],"area":[9],"in":[10,21,72],"the":[11,44,51,55,116,120,138,145],"emotion":[12,85],"and":[14,33,100,108,126,134,159,163],"computer":[15],"vision.":[16],"Applications":[17],"can":[18,152],"be":[19,47],"found":[20],"several":[22,34],"domains":[23],"such":[24],"as":[25],"medical":[26],"treatment,":[27],"driver":[28],"fatigue":[29],"surveillance,":[30],"sociable":[31],"robotics,":[32],"other":[35],"human-computer":[36],"interaction":[37],"systems.":[38],"Therefore,":[39],"it":[40],"crucial":[42],"that":[43,144],"machine":[45],"should":[46],"able":[48],"to":[49,114],"recognize":[50],"emotional":[52],"state":[53],"of":[54,118,157],"user":[56],"with":[57,69],"high":[58],"accuracy.":[59],"In":[60,75],"recent":[61],"years,":[62],"deep":[63],"neural":[64],"networks":[65],"have":[66,142],"been":[67],"used":[68],"great":[70],"success":[71],"recognizing":[73],"emotions.":[74],"this":[76],"paper,":[77],"we":[78],"present":[79],"a":[80],"new":[81],"model":[82],"for":[83,124,131,161],"continuous":[84],"based":[87,96,148],"on":[88,97,137,149],"FER":[89],"by":[90],"using":[91],"unsupervised":[93],"learning":[94,99],"approach":[95,104,147],"transfer":[98],"autoencoders.":[101],"The":[102],"proposed":[103,146],"also":[105],"includes":[106],"preprocessing":[107],"post-processing":[109],"techniques":[110],"which":[111],"contribute":[112],"favorably":[113],"improving":[115],"performance":[117],"predicting":[119,132],"concordance":[121,154],"correlation":[122,155],"coefficient":[123,156],"arousal":[125],"valence":[127,162],"dimensions.":[128],"Experimental":[129],"results":[130],"spontaneous":[133],"natural":[135],"emotions":[136],"RECOLA":[139],"2016":[140],"dataset":[141],"shown":[143],"visual":[150],"information":[151],"achieve":[153],"0.516":[158],"0.264":[160],"arousal,":[164],"respectively.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2022-07-26T00:00:00"}