{"id":"https://openalex.org/W7135406295","doi":"https://doi.org/10.1016/j.asoc.2026.114998","title":"A deep learning feature mapping algorithm for emotion detection via facial and audio signals","display_name":"A deep learning feature mapping algorithm for emotion detection via facial and audio signals","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7135406295","doi":"https://doi.org/10.1016/j.asoc.2026.114998"},"language":"en","primary_location":{"id":"doi:10.1016/j.asoc.2026.114998","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.114998","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.asoc.2026.114998","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004791600","display_name":"Mohammad-Hassan Tayarani-Najaran","orcid":"https://orcid.org/0000-0002-5999-2134"},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Mohammad Hassan Tayarani Najaran","raw_affiliation_strings":["University of Hertfordshire, Hatfield, UK"],"raw_orcid":"https://orcid.org/0000-0002-5999-2134","affiliations":[{"raw_affiliation_string":"University of Hertfordshire, Hatfield, UK","institution_ids":["https://openalex.org/I141584323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006625408","display_name":"Shamim Ibne Shahid","orcid":null},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shamim Ibne Shahid","raw_affiliation_strings":["University of Hertfordshire, Hatfield, UK"],"raw_orcid":"https://orcid.org/0009-0005-9174-5044","affiliations":[{"raw_affiliation_string":"University of Hertfordshire, Hatfield, UK","institution_ids":["https://openalex.org/I141584323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005754254","display_name":"Frank Foerster","orcid":"https://orcid.org/0000-0003-1797-682X"},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Frank Foerster","raw_affiliation_strings":["University of Hertfordshire, Hatfield, UK"],"raw_orcid":"https://orcid.org/0000-0003-1797-682X","affiliations":[{"raw_affiliation_string":"University of Hertfordshire, Hatfield, UK","institution_ids":["https://openalex.org/I141584323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015879027","display_name":"Volker Steuber","orcid":"https://orcid.org/0000-0003-0186-3580"},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Volker Steuber","raw_affiliation_strings":["University of Hertfordshire, Hatfield, UK"],"raw_orcid":"https://orcid.org/0000-0003-0186-3580","affiliations":[{"raw_affiliation_string":"University of Hertfordshire, Hatfield, UK","institution_ids":["https://openalex.org/I141584323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004791600"],"corresponding_institution_ids":["https://openalex.org/I141584323"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53112013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"197","issue":null,"first_page":"114998","last_page":"114998"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9926000237464905,"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":0.9926000237464905,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.0006000000284984708,"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"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/discriminative-model","display_name":"Discriminative model","score":0.8325999975204468},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6060000061988831},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6022999882698059},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5751000046730042},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5249000191688538},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5242999792098999},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4862000048160553},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.47699999809265137}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8325999975204468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8209999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6829000115394592},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6060000061988831},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6022999882698059},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5751000046730042},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5242999792098999},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4862000048160553},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3935999870300293},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3682999908924103},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3492000102996826},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.asoc.2026.114998","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.114998","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.asoc.2026.114998","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.114998","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7472772002220154,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W1948496279","https://openalex.org/W1974369784","https://openalex.org/W1977539641","https://openalex.org/W1993389465","https://openalex.org/W1994562938","https://openalex.org/W2003238582","https://openalex.org/W2004670864","https://openalex.org/W2004905543","https://openalex.org/W2030931454","https://openalex.org/W2070847871","https://openalex.org/W2094274886","https://openalex.org/W2137002202","https://openalex.org/W2138011226","https://openalex.org/W2156984202","https://openalex.org/W2166118664","https://openalex.org/W2348766510","https://openalex.org/W2516594906","https://openalex.org/W2625929003","https://openalex.org/W2761349923","https://openalex.org/W2799041689","https://openalex.org/W2803193013","https://openalex.org/W2946287218","https://openalex.org/W2946526173","https://openalex.org/W2948328975","https://openalex.org/W2951999654","https://openalex.org/W2964124680","https://openalex.org/W3003233130","https://openalex.org/W3008425820","https://openalex.org/W3010848533","https://openalex.org/W3022013598","https://openalex.org/W3033757068","https://openalex.org/W3037897005","https://openalex.org/W3114195756","https://openalex.org/W3139270985","https://openalex.org/W3157429286","https://openalex.org/W3165055457","https://openalex.org/W3203381531","https://openalex.org/W3207810231","https://openalex.org/W4210814395","https://openalex.org/W4210940289","https://openalex.org/W4224212321","https://openalex.org/W4233099317","https://openalex.org/W4297347970","https://openalex.org/W4362721809","https://openalex.org/W4376645477","https://openalex.org/W4391019654","https://openalex.org/W4391718355","https://openalex.org/W4401050382","https://openalex.org/W4404066606"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"emotion":[1,249],"recognition":[2],"plays":[3],"a":[4,55,72,85,114,242,254,264],"critical":[5],"role":[6],"in":[7,59,277],"areas":[8],"such":[9,223],"as":[10,224],"mental-health":[11],"monitoring,":[12,229],"human\u2013robot":[13],"interaction,":[14],"and":[15,29,81,133,164,181,217,230,244,292,309,318],"personalised":[16],"learning":[17],"systems,":[18,227],"yet":[19],"current":[20],"multimodal":[21,175,248],"approaches":[22],"often":[23],"struggle":[24],"with":[25,152],"high":[26],"intra-class":[27,89,268],"variability":[28],"the":[30,120,123,131,141,186,196,199,210,272,278,313],"limited":[31],"discriminative":[32,115],"power":[33],"of":[34,45,122,154,201,274,288],"raw":[35],"audio\u2013visual":[36,203],"features.":[37],"Existing":[38],"methods":[39],"typically":[40],"rely":[41],"on":[42,103,130,162,167],"direct":[43],"classification":[44],"audio":[46,80],"or":[47,107,171],"facial":[48],"data,":[49],"which":[50,60,100],"does":[51],"not":[52],"explicitly":[53,112],"enforce":[54],"structured":[56],"joint":[57,265],"embedding":[58],"emotional":[61,275],"categories":[62],"become":[63],"separable.":[64],"This":[65],"paper":[66],"addresses":[67],"this":[68],"limitation":[69],"by":[70],"proposing":[71],"supervised":[73,255],"contrastive":[74,238,256],"feature-mapping":[75],"algorithm":[76,301],"that":[77,87,118,140,185,237,258],"transforms":[78],"temporal":[79,208,260],"video":[82],"features":[83,262],"into":[84,263],"representation":[86,188],"minimises":[88],"distances":[90],"while":[91,169],"maximising":[92],"inter-class":[93],"distances.":[94],"In":[95],"contrast":[96],"to":[97,156,220,270,306],"prior":[98],"work,":[99],"usually":[101],"focuses":[102],"handcrafted":[104],"feature":[105,124,212,239,280,289],"engineering":[106],"end-to-end":[108],"classifiers,":[109],"our":[110],"approach":[111],"learns":[113],"metric":[116],"space":[117,213],"enhances":[119],"geometry":[121],"distribution.":[125],"The":[126],"method":[127,197],"is":[128,214],"evaluated":[129],"RAVDESS":[132,163],"CREMA-D":[134],"benchmark":[135],"datasets.":[136],"Experimental":[137],"results":[138,235],"show":[139],"proposed":[142],"mapping":[143,240],"yields":[144],"consistent":[145],"accuracy":[146,161],"improvements":[147],"over":[148],"strong":[149],"machine-learning":[150],"baselines,":[151],"gains":[153],"up":[155],"approximately":[157],"6%,":[158],"achieving":[159],"96.07%":[160],"competitive":[165],"performance":[166,304],"CREMA-D,":[168],"outperforming":[170],"matching":[172],"recent":[173],"state-of-the-art":[174],"emotion-recognition":[176],"pipelines.":[177],"Statistical":[178],"tests":[179],"(Kruskal\u2013Wallis":[180],"paired":[182,202],"t-tests)":[183],"confirm":[184],"learned":[187,211],"significantly":[189],"increases":[190],"class":[191,297],"separability":[192,273],"(":[193],").":[194],"While":[195],"assumes":[198],"availability":[200],"inputs":[204],"without":[205],"requiring":[206],"explicit":[207],"alignment,":[209],"compact,":[215],"discriminative,":[216],"well":[218],"suited":[219],"downstream":[221],"tasks":[222],"affect-aware":[225],"dialogue":[226],"rehabilitation":[228],"adaptive":[231],"educational":[232],"interfaces.":[233],"These":[234],"demonstrate":[236],"provides":[241],"robust":[243],"generalisable":[245],"framework":[246],"for":[247,315],"analysis.":[250],"\u2022":[251,282,299],"We":[252,283],"propose":[253],"model":[257],"maps":[259],"audio-visual":[261],"representation,":[266],"minimizing":[267],"variations":[269],"enhance":[271],"classes":[276],"transformed":[279],"space.":[281],"provide":[284],"an":[285],"in-depth":[286],"analysis":[287],"distributions":[290],"before":[291],"after":[293],"transformation,":[294],"demonstrating":[295],"improved":[296],"discrimination.":[298],"Our":[300],"achieves":[302],"superior":[303],"compared":[305],"existing":[307],"approaches,":[308],"we":[310],"publicly":[311],"release":[312],"software":[314],"further":[316],"research":[317],"development.":[319]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-03-15T00:00:00"}
