{"id":"https://openalex.org/W7123335258","doi":"https://doi.org/10.1109/access.2026.3652829","title":"Are Synthetic Data as Good as Real Data for Recognizing Doppelg\u00e4ngers and Twins Faces?","display_name":"Are Synthetic Data as Good as Real Data for Recognizing Doppelg\u00e4ngers and Twins Faces?","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123335258","doi":"https://doi.org/10.1109/access.2026.3652829"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3652829","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652829","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3652829","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085815591","display_name":"Bernardo Biesseck","orcid":"https://orcid.org/0000-0003-0998-6913"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Bernardo Biesseck","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122854466","display_name":"Pedro Vidal","orcid":null},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Pedro Vidal","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Luiz E. L. Coelho","orcid":"https://orcid.org/0009-0004-8330-5199"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz E. L. Coelho","raw_affiliation_strings":["Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122894248","display_name":"Roger Granada","orcid":null},"institutions":[{"id":"https://openalex.org/I2970213756","display_name":"Alltech (United States)","ror":"https://ror.org/01gh6ja41","country_code":"US","type":"company","lineage":["https://openalex.org/I2970213756"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roger Granada","raw_affiliation_strings":["Unico IDTech, S&#x00C3;o Paulo, Brazil"],"affiliations":[{"raw_affiliation_string":"Unico IDTech, S&#x00C3;o Paulo, Brazil","institution_ids":["https://openalex.org/I2970213756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122895585","display_name":"Ruben Tolosana","orcid":null},"institutions":[{"id":"https://openalex.org/I169724904","display_name":"Zimmer Biomet (Netherlands)","ror":"https://ror.org/034k8cv93","country_code":"NL","type":"company","lineage":["https://openalex.org/I169724904","https://openalex.org/I4210115238"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ruben Tolosana","raw_affiliation_strings":["Biometrics and Data Pattern Analytics Laboratory&#x2014;BiDA-Laboratory, Universidad Aut&#x00F3;noma de Madrid, Madrid, Spain"],"affiliations":[{"raw_affiliation_string":"Biometrics and Data Pattern Analytics Laboratory&#x2014;BiDA-Laboratory, Universidad Aut&#x00F3;noma de Madrid, Madrid, Spain","institution_ids":["https://openalex.org/I169724904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002070764","display_name":"David Menotti","orcid":null},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"David Menotti","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1;, Curitiba, Paran\u00e1, Brazil","institution_ids":["https://openalex.org/I52418104"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5085815591"],"corresponding_institution_ids":["https://openalex.org/I52418104"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0686907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"9961","last_page":"9974"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9908000230789185,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9908000230789185,"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/T11094","display_name":"Face Recognition and Perception","score":0.0017000000225380063,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0015999999595806003,"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/synthetic-data","display_name":"Synthetic data","score":0.8356999754905701},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5117999911308289},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.504800021648407},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49309998750686646},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.45179998874664307},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.44020000100135803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4383000135421753},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.38019999861717224}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.8356999754905701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8004999756813049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6381000280380249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5181000232696533},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5117999911308289},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.504800021648407},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49309998750686646},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.45179998874664307},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.44020000100135803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3652829","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652829","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3652829","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652829","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.792037308216095,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[{"id":"https://openalex.org/G2065527848","display_name":null,"funder_award_id":"315409/2023-1","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"},{"id":"https://openalex.org/G4210048909","display_name":null,"funder_award_id":"PDSE #88881.982469/2024-01","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"},{"id":"https://openalex.org/G6479949120","display_name":null,"funder_award_id":"#315409/2023-1","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320313831","display_name":"Comunidad de Madrid","ror":null},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320324317","display_name":"Instituto Federal de Mato Grosso","ror":"https://ror.org/02t6f2351"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1983197836","https://openalex.org/W2029600384","https://openalex.org/W2137659841","https://openalex.org/W2194775991","https://openalex.org/W2311038409","https://openalex.org/W2404498690","https://openalex.org/W2515770085","https://openalex.org/W2663800299","https://openalex.org/W2799209711","https://openalex.org/W2962770929","https://openalex.org/W2962898354","https://openalex.org/W2963460857","https://openalex.org/W2969985801","https://openalex.org/W3034371424","https://openalex.org/W3034552680","https://openalex.org/W3167584510","https://openalex.org/W3169129566","https://openalex.org/W3201790025","https://openalex.org/W3204715535","https://openalex.org/W4214588288","https://openalex.org/W4292755595","https://openalex.org/W4312402191","https://openalex.org/W4312769845","https://openalex.org/W4319300360","https://openalex.org/W4320063407","https://openalex.org/W4386083096","https://openalex.org/W4389169878","https://openalex.org/W4390190198","https://openalex.org/W4390871781","https://openalex.org/W4390873054","https://openalex.org/W4390873484","https://openalex.org/W4392411901","https://openalex.org/W4392454913","https://openalex.org/W4400527557","https://openalex.org/W4402523417","https://openalex.org/W4404893185","https://openalex.org/W4406461747","https://openalex.org/W4408459025"],"related_works":[],"abstract_inverted_index":{"Synthetic":[0],"face":[1,163],"datasets":[2,120,124,164,193],"for":[3],"training":[4],"Face":[5],"Recognition":[6],"(FR)":[7],"models":[8,54,65,114,182],"have":[9,40],"gained":[10],"significant":[11],"interest":[12],"in":[13,27,88,167,180,206],"recent":[14],"years":[15],"due":[16],"to":[17,42,62,148],"privacy":[18],"concerns":[19],"associated":[20],"with":[21,94],"real":[22,68,117,162,187,201],"facial":[23,96,128],"data.":[24],"Recent":[25],"advances":[26],"generative":[28],"techniques,":[29],"based":[30],"mainly":[31],"on":[32,56,67,72,116,172,184],"Generative":[33],"Adversarial":[34],"Networks":[35],"(GANs)":[36],"and":[37,80,102,134,146,161,213],"Diffusion":[38],"Models,":[39],"led":[41],"the":[43,50,157,198],"proliferation":[44],"of":[45,52,64,200],"various":[46],"synthetic":[47,57,119,160,185,192],"datasets.":[48],"Although":[49],"performance":[51],"FR":[53,113,181],"trained":[55,66,115,183,211],"data":[58,69,202],"is":[59,83,165],"becoming":[60],"similar":[61,178],"those":[63],"when":[70],"evaluated":[71],"standard":[73],"benchmarks":[74],"such":[75,98],"as":[76,99],"LFW,":[77],"CFP-FP,":[78],"AgeDB,":[79],"CALFW,":[81],"little":[82],"known":[84],"about":[85],"their":[86,207],"effectiveness":[87],"more":[89],"challenging":[90,169],"scenarios":[91,170],"involving":[92],"individuals":[93],"high":[95,127],"similarity,":[97],"doppelg\u00e4ngers":[100],"(lookalikes)":[101],"identical":[103],"twins.":[104],"This":[105],"work":[106],"addresses":[107],"this":[108],"gap":[109,158],"by":[110,143],"evaluating":[111],"state-of-the-art":[112],"or":[118,186],"across":[121],"four":[122],"testing":[123],"that":[125,156,190],"feature":[126],"similarity:":[129],"HDA":[130],"Doppelg\u00e4nger,":[131],"DoppelVer,":[132],"3D-TEC,":[133],"ND-Twins-2009-2010.":[135],"We":[136,175],"analyze":[137],"verification":[138],"performances,":[139],"including":[140],"subgroup":[141],"analysis":[142],"ethnicity,":[144],"gender,":[145],"age,":[147],"better":[149],"understand":[150],"demographic":[151],"disparities.":[152],"Our":[153],"results":[154],"show":[155],"between":[159],"larger":[166],"these":[168],"than":[171],"typical":[173],"benchmarks.":[174],"also":[176],"observe":[177],"biases":[179,196],"data,":[188],"suggesting":[189],"current":[191],"may":[194],"inherit":[195],"through":[197],"use":[199],"at":[203],"some":[204],"stage":[205],"pipeline.":[208],"Reproducibility":[209],"code,":[210],"models,":[212],"instructions":[214],"are":[215],"available":[216],"at.":[217]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-14T00:00:00"}
