{"id":"https://openalex.org/W7116949011","doi":"https://doi.org/10.1109/tcss.2025.3638859","title":"A Survey on Deep Learning for Group-Level Emotion Recognition","display_name":"A Survey on Deep Learning for Group-Level Emotion Recognition","publication_year":2025,"publication_date":"2025-12-23","ids":{"openalex":"https://openalex.org/W7116949011","doi":"https://doi.org/10.1109/tcss.2025.3638859"},"language":null,"primary_location":{"id":"doi:10.1109/tcss.2025.3638859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2025.3638859","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social Systems","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":null,"display_name":"Xiaohua Huang","orcid":"https://orcid.org/0000-0001-8897-3517"},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohua Huang","raw_affiliation_strings":["Oulu School, Nanjing Institute of Technology, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0001-8897-3517","affiliations":[{"raw_affiliation_string":"Oulu School, Nanjing Institute of Technology, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I2799736854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121124223","display_name":"Xiaopeng Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Hong","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0002-0611-0636","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039208882","display_name":"Qirong Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qirong Mao","raw_affiliation_strings":["School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-0616-4431","affiliations":[{"raw_affiliation_string":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121060844","display_name":"Wenming Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090971","display_name":"Southeast University","ror":"https://ror.org/00cf0ab87","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210090971"]},{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["BD","CN"],"is_corresponding":false,"raw_author_name":"Wenming Zheng","raw_affiliation_strings":["Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, and the School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-7764-5179","affiliations":[{"raw_affiliation_string":"Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, and the School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877","https://openalex.org/I4210090971"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085376429","display_name":"Abhinav Dhall","orcid":"https://orcid.org/0000-0002-2230-1440"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abhinav Dhall","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-2230-1440","affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0445,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.95654309,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":"2","first_page":"2475","last_page":"2500"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9896000027656555,"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":0.9896000027656555,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.0017000000225380063,"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/T10709","display_name":"Social Robot Interaction and HRI","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/deep-learning","display_name":"Deep learning","score":0.7613999843597412},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.39570000767707825},{"id":"https://openalex.org/keywords/multimodality","display_name":"Multimodality","score":0.38019999861717224},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.36010000109672546},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.3596000075340271},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.35370001196861267},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3497999906539917},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7613999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6297000050544739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6004999876022339},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.3167000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcss.2025.3638859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2025.3638859","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2369429548","display_name":null,"funder_award_id":"62576155","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6945706764","display_name":null,"funder_award_id":"62076122","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7158469337","display_name":null,"funder_award_id":"62176106","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,30,38,49,68,80,144,169],"rapid":[2],"advancement":[3],"of":[4,33,48,72,100,104,146,171],"artificial":[5],"intelligence,":[6],"group-level":[7],"emotion":[8,62],"recognition":[9],"(GER)":[10],"has":[11,36],"emerged":[12],"as":[13,181],"an":[14],"important":[15],"domain":[16],"in":[17,55,188],"human":[18],"behavior":[19],"analysis.":[20],"Early":[21],"GER":[22,64,130,174],"methods":[23,86],"primarily":[24],"relied":[25],"on":[26],"handcrafted":[27],"features.":[28],"However,":[29],"recent":[31,137],"success":[32],"deep":[34,84,101],"learning":[35],"shifted":[37],"focus":[39],"toward":[40],"neural":[41],"network-based":[42],"solution,":[43],"enabling":[44],"more":[45],"effective":[46],"exploitation":[47],"rich":[50],"visual":[51],"and":[52,58,70,117,120,132,149,161],"contextual":[53],"cues":[54],"group":[56],"images":[57],"videos.":[59],"Unlike":[60],"individual-level":[61],"recognition,":[63],"must":[65],"account":[66],"for":[67,185],"diversity":[69],"dynamics":[71],"multiple":[73],"individuals":[74],"within":[75],"varied":[76],"social":[77],"contexts.":[78],"Over":[79],"past":[81],"decade,":[82],"numerous":[83],"learning-based":[85],"have":[87],"been":[88],"proposed,":[89],"achieving":[90],"substantial":[91],"performance":[92,134],"gains.":[93],"This":[94,176],"survey":[95],"provides":[96],"a":[97,107,182],"comprehensive":[98],"review":[99,103],"learning-centric":[102],"GER,":[105],"introducing":[106],"new":[108],"taxonomy":[109],"that":[110],"spans":[111],"representation":[112],"learning,":[113],"graph-based":[114],"modeling,":[115],"attention":[116],"transformer":[118],"architectures,":[119],"multimodal":[121,153],"fusion":[122],"strategies.":[123],"We":[124],"summarize":[125],"benchmark":[126],"datasets,":[127],"outline":[128],"prevailing":[129],"pipelines,":[131],"consolidate":[133],"trends":[135],"from":[136],"state-of-the-art":[138],"approaches.":[139],"In":[140],"addition,":[141],"we":[142],"discuss":[143],"integration":[145],"foundation":[147],"models":[148],"large":[150],"language":[151],"model-guided":[152],"reasoning":[154],"into":[155],"GER.":[156],"Key":[157],"challenges":[158],"are":[159,165],"identified,":[160],"potential":[162],"research":[163,187],"directions":[164],"proposed":[166],"to":[167,179],"support":[168],"development":[170],"robust,":[172],"real-world":[173],"systems.":[175],"work":[177],"aims":[178],"serve":[180],"pivotal":[183],"reference":[184],"future":[186],"this":[189],"evolving":[190],"field.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":4}],"updated_date":"2026-04-03T16:38:21.277918","created_date":"2025-12-23T00:00:00"}
