{"id":"https://openalex.org/W4317039517","doi":"https://doi.org/10.3233/idt-220301","title":"Multi-level graded facial emotion intensity recognition using MCANN for health care","display_name":"Multi-level graded facial emotion intensity recognition using MCANN for health care","publication_year":2023,"publication_date":"2023-01-17","ids":{"openalex":"https://openalex.org/W4317039517","doi":"https://doi.org/10.3233/idt-220301"},"language":"en","primary_location":{"id":"doi:10.3233/idt-220301","is_oa":false,"landing_page_url":"https://doi.org/10.3233/idt-220301","pdf_url":null,"source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-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/A5055346305","display_name":"Nazmin Begum","orcid":"https://orcid.org/0000-0002-7456-0911"},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nazmin Begum","raw_affiliation_strings":["Department of ISE, HKBK College of Engineering, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of ISE, HKBK College of Engineering, Bangalore, India","institution_ids":["https://openalex.org/I4387152553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076777740","display_name":"A Syed Mustafa","orcid":"https://orcid.org/0000-0001-9588-9452"},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"A. Syed Mustafa","raw_affiliation_strings":["Department of ISE, HKBK College of Engineering, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of ISE, HKBK College of Engineering, Bangalore, India","institution_ids":["https://openalex.org/I4387152553"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055346305"],"corresponding_institution_ids":["https://openalex.org/I4387152553"],"apc_list":null,"apc_paid":null,"fwci":0.2349,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53857462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"17","issue":"2","first_page":"331","last_page":"341"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9947999715805054,"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":0.9937000274658203,"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/disgust","display_name":"Disgust","score":0.8833034634590149},{"id":"https://openalex.org/keywords/sadness","display_name":"Sadness","score":0.7983875274658203},{"id":"https://openalex.org/keywords/anger","display_name":"Anger","score":0.6757119297981262},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.6451290845870972},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6078341007232666},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5996062755584717},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5612828731536865},{"id":"https://openalex.org/keywords/amusement","display_name":"Amusement","score":0.4796062707901001},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4622231721878052},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.43379056453704834},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4212527871131897},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42102938890457153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4130017161369324},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3340511918067932},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3182012438774109},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.09060508012771606}],"concepts":[{"id":"https://openalex.org/C2777375102","wikidata":"https://www.wikidata.org/wiki/Q208351","display_name":"Disgust","level":3,"score":0.8833034634590149},{"id":"https://openalex.org/C2779812673","wikidata":"https://www.wikidata.org/wiki/Q169251","display_name":"Sadness","level":3,"score":0.7983875274658203},{"id":"https://openalex.org/C2779302386","wikidata":"https://www.wikidata.org/wiki/Q79871","display_name":"Anger","level":2,"score":0.6757119297981262},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.6451290845870972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6078341007232666},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5996062755584717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5612828731536865},{"id":"https://openalex.org/C2779056813","wikidata":"https://www.wikidata.org/wiki/Q2844542","display_name":"Amusement","level":2,"score":0.4796062707901001},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4622231721878052},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.43379056453704834},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4212527871131897},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42102938890457153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4130017161369324},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3340511918067932},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3182012438774109},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.09060508012771606},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/idt-220301","is_oa":false,"landing_page_url":"https://doi.org/10.3233/idt-220301","pdf_url":null,"source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1871419576","https://openalex.org/W1921147789","https://openalex.org/W1987231241","https://openalex.org/W1995937139","https://openalex.org/W2017745767","https://openalex.org/W2104883350","https://openalex.org/W2139795045","https://openalex.org/W2145287260","https://openalex.org/W2158419735","https://openalex.org/W2624789303","https://openalex.org/W2811041611","https://openalex.org/W2946273372","https://openalex.org/W2946704636","https://openalex.org/W2972660800","https://openalex.org/W2985108241","https://openalex.org/W2990218048","https://openalex.org/W3016138882","https://openalex.org/W3128513378","https://openalex.org/W3133232528","https://openalex.org/W4220672327","https://openalex.org/W4230277160","https://openalex.org/W4311970234","https://openalex.org/W6638667902","https://openalex.org/W6640072470","https://openalex.org/W6675362608","https://openalex.org/W6681342084","https://openalex.org/W6739286727","https://openalex.org/W6809862922"],"related_works":["https://openalex.org/W2794357331","https://openalex.org/W4238520549","https://openalex.org/W3216173459","https://openalex.org/W4242611441","https://openalex.org/W4242034606","https://openalex.org/W4250499761","https://openalex.org/W2037174948","https://openalex.org/W2031828545","https://openalex.org/W2143086761","https://openalex.org/W2519456985"],"abstract_inverted_index":{"Facial":[0,105,164],"emotion":[1,138],"recognition":[2,43],"analysis":[3,49,136],"is":[4,121,145],"widely":[5],"used":[6],"in":[7,44,55],"various":[8],"social":[9],"fields,":[10],"including":[11],"Law":[12],"Enforcement":[13],"for":[14,20,134],"police":[15],"interrogation,":[16],"virtual":[17],"assistants,":[18],"hospitals":[19],"understanding":[21],"patients\u2019":[22],"expressions,":[23],"etc.":[24],"In":[25,78],"the":[26,119,135],"field":[27],"of":[28,70,90,132,137,142],"medical":[29],"treatment":[30],"such":[31],"as":[32,53,103],"psychologically":[33],"affected":[34,58],"patients,":[35],"patients":[36],"undergoing":[37],"difficult":[38],"surgeries,":[39],"etc":[40],"require":[41],"emotional":[42,48,98],"real-time.":[45],"The":[46,127,140],"current":[47],"employs":[50],"interest":[51],"points":[52],"landmarks":[54],"facial":[56,88,97],"images":[57],"by":[59],"a":[60,83],"few":[61],"emotions":[62,71,89],"Many":[63],"researchers":[64],"have":[65],"proposed":[66,96,128],"7":[67],"different":[68,92,125],"types":[69,93],"(amusement,":[72],"anger,":[73],"disgust,":[74],"fear,":[75],"and":[76,155,174],"sadness).":[77],"our":[79,95,112,143],"work,":[80],"we":[81],"propose":[82],"deep":[84],"learning-based":[85],"multi-level":[86],"graded":[87],"21":[91],"with":[94,157,162],"feature":[99],"extraction":[100],"technique":[101],"called":[102],"Deep":[104],"Action":[106],"Extraction":[107],"Units":[108],"(DFAEU).":[109],"Then":[110],"using":[111,147],"Multi-Class":[113],"Artificial":[114],"Neural":[115],"Network":[116],"(MCANN)":[117],"architecture":[118],"model":[120,144,170],"trained":[122],"to":[123],"classify":[124],"emotions.":[126],"method":[129],"makes":[130],"use":[131],"VGG-16":[133],"grades.":[139],"performance":[141],"evaluated":[146],"two":[148],"algorithms":[149],"Sparse":[150],"Batch":[151],"Normalization":[152],"CNN":[153,156],"(SBN-CNN)":[154],"Attention":[158],"mechanism":[159],"(ACNN)":[160],"along":[161],"datasets":[163],"Emotion":[165],"Recognition":[166],"Challenge":[167],"(FERC-2013).":[168],"Our":[169],"outperforms":[171],"86.34":[172],"percent":[173,176],"98.6":[175],"precision.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-10-10T00:00:00"}
