{"id":"https://openalex.org/W7164888442","doi":"https://doi.org/10.1145/3785353.3815072","title":"Leveraging Synthetic Data to Reduce Biases in Face Recognition Systems","display_name":"Leveraging Synthetic Data to Reduce Biases in Face Recognition Systems","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7164888442","doi":"https://doi.org/10.1145/3785353.3815072"},"language":"en","primary_location":{"id":"doi:10.1145/3785353.3815072","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785353.3815072","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Workshop on Information Hiding and Multimedia Security","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3785353.3815072","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055392319","display_name":"Roberto Leyva","orcid":"https://orcid.org/0000-0003-4561-8798"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Roberto Leyva","raw_affiliation_strings":["University of Warwick, Coventry, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-4561-8798","affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, United Kingdom","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080906609","display_name":"P.A. Selvaraj","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Praveen Selvaraj","raw_affiliation_strings":["The Alan Turing Institute, London, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0004-9760-7112","affiliations":[{"raw_affiliation_string":"The Alan Turing Institute, London, United Kingdom","institution_ids":["https://openalex.org/I4210128584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080175512","display_name":"Carsten Maple","orcid":"https://orcid.org/0000-0002-4715-212X"},"institutions":[{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Carsten Maple","raw_affiliation_strings":["The Alan Turing Institute, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-4715-212X","affiliations":[{"raw_affiliation_string":"The Alan Turing Institute, London, United Kingdom","institution_ids":["https://openalex.org/I4210128584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100634965","display_name":"V\u00edctor S\u00e1nchez","orcid":"https://orcid.org/0000-0002-7089-7031"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Victor Sanchez","raw_affiliation_strings":["University of Warwick, Coventry, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-7089-7031","affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, United Kingdom","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74985254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9559000134468079,"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":0.9559000134468079,"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/T10057","display_name":"Face and Expression Recognition","score":0.009700000286102295,"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/T10828","display_name":"Biometric Identification and Security","score":0.008500000461935997,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/boosting","display_name":"Boosting (machine learning)","score":0.7688999772071838},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6796000003814697},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5771999955177307},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5331000089645386},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4984999895095825},{"id":"https://openalex.org/keywords/suspect","display_name":"Suspect","score":0.4950000047683716},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.43059998750686646},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.41760000586509705}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7688999772071838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7599999904632568},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6796000003814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5778999924659729},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5771999955177307},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C2778223634","wikidata":"https://www.wikidata.org/wiki/Q224952","display_name":"Suspect","level":2,"score":0.4950000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48989999294281006},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2776552730","wikidata":"https://www.wikidata.org/wiki/Q189656","display_name":"Disinformation","level":3,"score":0.352400004863739},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33219999074935913},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.27469998598098755},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3785353.3815072","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785353.3815072","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Workshop on Information Hiding and Multimedia Security","raw_type":"proceedings-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:201725","is_oa":true,"landing_page_url":null,"pdf_url":"https://wrap.warwick.ac.uk/id/eprint/201725/2/WRAP-Leveraging-synthetic-data-reduce-biases-face-recognition-systems-26.pdf","source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Item"}],"best_oa_location":{"id":"doi:10.1145/3785353.3815072","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785353.3815072","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Workshop on Information Hiding and Multimedia Security","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.5607295036315918}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W3033534610","https://openalex.org/W3034256900","https://openalex.org/W3161922222","https://openalex.org/W3162922192","https://openalex.org/W3165157970","https://openalex.org/W3175998650","https://openalex.org/W3181414820","https://openalex.org/W3199041638","https://openalex.org/W3216572446","https://openalex.org/W3216710733","https://openalex.org/W4211155626","https://openalex.org/W4224286436","https://openalex.org/W4312940830","https://openalex.org/W4322576666","https://openalex.org/W4385800961","https://openalex.org/W4386242365","https://openalex.org/W4386246891","https://openalex.org/W4390406141","https://openalex.org/W4390872942","https://openalex.org/W4392122172","https://openalex.org/W4398174331","https://openalex.org/W4400114578","https://openalex.org/W4404613713","https://openalex.org/W4410600831"],"related_works":[],"abstract_inverted_index":{"Current":[0],"generative":[1],"technologies":[2],"have":[3],"enabled":[4],"the":[5,28,41,77,93,123,130,143,163,186],"creation":[6],"of":[7,30,43,82,149,155,169,188],"realistic":[8],"synthetic":[9,16,56,99],"media,":[10],"including":[11,179],"face":[12,57],"images.":[13],"Although":[14],"such":[15,111],"data":[17,35,79],"may":[18],"pose":[19],"a":[20,52,146,153,167],"threat":[21],"when":[22],"used":[23,103],"for":[24,96,166,181],"malicious":[25],"purposes,":[26],"e.g.,":[27],"spread":[29],"disinformation":[31],"or":[32],"impersonation,":[33],"these":[34],"can":[36,141,161],"be":[37],"leveraged":[38],"to":[39,59,67,74,128,151],"improve":[40],"performance":[42,165],"Face":[44],"Recognition":[45],"Systems":[46],"(FRS).":[47],"This":[48,133],"paper":[49],"then":[50],"proposes":[51],"strategy":[53,91,119,134],"that":[54],"leverages":[55],"images":[58],"reduce":[60,68],"biases":[61],"in":[62,64,76,80],"FRS;":[63],"other":[65,86],"words,":[66],"unexpected":[69],"and":[70,85,158],"incorrect":[71],"results":[72],"due":[73],"imbalances":[75],"training":[78,97,108,114,121],"terms":[81],"ethnicity,":[83],"gender,":[84],"human":[87],"traits.":[88],"The":[89],"proposed":[90],"balances":[92],"samples":[94],"available":[95],"with":[98],"images,":[100],"which":[101,152],"are":[102],"exclusively":[104],"during":[105],"an":[106,112],"initial":[107,113],"process.":[109],"Once":[110],"process":[115],"is":[116],"complete,":[117],"our":[118,189],"resumes":[120],"on":[122,176],"reference":[124],"dataset":[125],"(real":[126],"data)":[127],"learn":[129],"target":[131],"identities.":[132],"has":[135],"two":[136],"main":[137],"advantages:":[138],"(1)":[139],"it":[140,160],"bias":[142],"model":[144],"towards":[145],"specific":[147],"group":[148],"people":[150],"person":[154,168],"interest":[156,170],"belongs,":[157],"(2)":[159],"increase":[162],"authentication":[164],"by":[171],"boosting":[172],"specificity.":[173],"Our":[174],"experiments":[175],"several":[177],"datasets,":[178],"one":[180],"crime":[182],"suspect":[183],"authentication,":[184],"confirm":[185],"advantages":[187],"strategy.":[190]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-06-17T00:00:00"}
