{"id":"https://openalex.org/W7161675909","doi":"https://doi.org/10.48550/arxiv.2605.17483","title":"On Applicability of Synthetic Datasets for Facial Expression Recognition","display_name":"On Applicability of Synthetic Datasets for Facial Expression Recognition","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161675909","doi":"https://doi.org/10.48550/arxiv.2605.17483"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17483","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17483","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.17483","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115395646","display_name":"Ali Azmoudeh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azmoudeh, Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109729568","display_name":"Erdi Sar\u0131ta\u015f","orcid":"https://orcid.org/0009-0001-0493-6792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sar\u0131ta\u015f, Erdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136500668","display_name":"\u00d6mer Y\u0131ld\u0131r\u0131m","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Y\u0131ld\u0131r\u0131m, \u00d6mer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136469926","display_name":"Haz\u0131m Kemal Ekenel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ekenel, Haz\u0131m Kemal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8026999831199646,"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":0.8026999831199646,"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/T11448","display_name":"Face recognition and analysis","score":0.09600000083446503,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.015699999406933784,"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/facial-expression","display_name":"Facial expression","score":0.7024999856948853},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6434999704360962},{"id":"https://openalex.org/keywords/facial-expression-recognition","display_name":"Facial expression recognition","score":0.5333999991416931},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.5213000178337097},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.47530001401901245},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.47440001368522644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4523000121116638},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44780001044273376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7430999875068665},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.7024999856948853},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6434999704360962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5860999822616577},{"id":"https://openalex.org/C2987714656","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Facial expression recognition","level":4,"score":0.5333999991416931},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.5213000178337097},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4415999948978424},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.38429999351501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2874999940395355},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C54654163","wikidata":"https://www.wikidata.org/wiki/Q5428359","display_name":"Face hallucination","level":5,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17483","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17483","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17483","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17483","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Facial":[0],"Expression":[1],"Recognition":[2],"faces":[3],"two":[4],"core":[5],"challenges.":[6],"The":[7,23,154,193],"first":[8],"is":[9,25],"class":[10],"imbalance":[11,210],"in":[12,62,190],"public":[13],"datasets,":[14,121],"which":[15,33],"skews":[16],"the":[17,35,42,63,129,145,149,185],"learning":[18],"process":[19],"and":[20,29,40,98,111,115,125,134,165,178,211,215],"weakens":[21],"generalization.":[22],"second":[24],"related":[26],"to":[27,174,208],"privacy":[28,212],"data":[30,198],"collection":[31],"constraints,":[32],"limit":[34],"sharing":[36],"of":[37,44],"facial":[38,67,106],"images":[39],"restrict":[41],"creation":[43],"large,":[45],"balanced":[46],"datasets.":[47,192],"To":[48],"address":[49],"these":[50],"issues,":[51],"we":[52,117,143,166,183],"examine":[53],"three":[54],"complementary":[55],"strategies":[56,72],"for":[57,140,151],"constructing":[58],"privacy-preserving":[59],"FER":[60],"datasets":[61,131,207],"standard":[64],"seven":[65],"discrete":[66],"expression":[68,102,107],"classes":[69],"setting.":[70],"Our":[71],"are:":[73],"(i)":[74],"pseudo-labeling":[75],"large":[76],"unlabeled":[77,138],"face":[78],"collections":[79],"with":[80,205],"a":[81,85,160,169],"teacher":[82],"model":[83],"under":[84],"confidence-thresholding":[86],"scheme,":[87],"(ii)":[88],"prompt-driven":[89],"synthesis":[90],"using":[91,159],"diffusion":[92],"models":[93],"conditioned":[94],"on":[95],"demographic":[96],"attributes,":[97],"(iii)":[99],"task-aware":[100],"GAN-based":[101],"editing":[103],"that":[104],"modifies":[105],"while":[108],"preserving":[109],"identity":[110],"realism.":[112],"For":[113],"training":[114],"evaluation,":[116],"employed":[118],"widely":[119],"adopted":[120],"including":[122],"AffectNet,":[123],"RAF-DB,":[124],"FER2013.":[126],"We":[127],"utilized":[128,144],"synthetic":[130,197],"DigiFace,":[132],"DCFace,":[133],"EmoNet-Face":[135],"BIG":[136],"as":[137,148],"sources":[139],"pseudo-labeling.":[141],"Additionally,":[142],"FFHQ":[146],"dataset":[147],"source":[150],"generative":[152],"synthesis.":[153],"main":[155],"experiments":[156],"are":[157],"conducted":[158],"classic":[161],"CNN":[162],"backbone,":[163],"IR50,":[164],"also":[167],"explore":[168],"more":[170],"complex":[171],"architecture,":[172],"POSTERv1,":[173],"assess":[175],"its":[176],"feasibility":[177],"robustness.":[179],"Using":[180],"cross-dataset":[181],"evaluations,":[182],"analyze":[184],"trade-offs":[186],"each":[187],"strategy":[188],"presents":[189],"curated":[191],"findings":[194],"demonstrate":[195],"how":[196],"can":[199],"effectively":[200],"substitute":[201],"or":[202],"be":[203],"combined":[204],"real":[206],"mitigate":[209],"limitations.":[213],"Code":[214],"generated":[216],"datasets:https://www.github.com/AliAZ98/SyntFER":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
