{"id":"https://openalex.org/W3117739277","doi":"https://doi.org/10.23919/eusipco47968.2020.9287219","title":"Analysis of Race and Gender Bias in Deep Age Estimation Models","display_name":"Analysis of Race and Gender Bias in Deep Age Estimation Models","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3117739277","doi":"https://doi.org/10.23919/eusipco47968.2020.9287219","mag":"3117739277"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287219","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-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":"https://openalex.org/A5051648741","display_name":"Puc Andraz","orcid":null},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":true,"raw_author_name":"Andraz Puc","raw_affiliation_strings":["Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038322250","display_name":"Vitomir \u0160truc","orcid":"https://orcid.org/0000-0002-3385-5780"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Vitomir Struc","raw_affiliation_strings":["Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009780279","display_name":"Klemen Grm","orcid":"https://orcid.org/0000-0002-3637-8182"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Klemen Grm","raw_affiliation_strings":["Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia","institution_ids":["https://openalex.org/I153976015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051648741"],"corresponding_institution_ids":["https://openalex.org/I153976015"],"apc_list":null,"apc_paid":null,"fwci":1.4721,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.85125008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"830","last_page":"834"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":1.0,"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":1.0,"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":0.9907000064849854,"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/T11322","display_name":"Facial Rejuvenation and Surgery Techniques","score":0.9574000239372253,"subfield":{"id":"https://openalex.org/subfields/2708","display_name":"Dermatology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.742895245552063},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7154882550239563},{"id":"https://openalex.org/keywords/race","display_name":"Race (biology)","score":0.7079892158508301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6788918375968933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5860440135002136},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.5770776271820068},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5198788642883301},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4830354154109955},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4498426914215088},{"id":"https://openalex.org/keywords/age-groups","display_name":"Age groups","score":0.44488200545310974},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4429628551006317},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44229137897491455},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.16471371054649353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14484402537345886}],"concepts":[{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.742895245552063},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7154882550239563},{"id":"https://openalex.org/C76509639","wikidata":"https://www.wikidata.org/wiki/Q918036","display_name":"Race (biology)","level":2,"score":0.7079892158508301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6788918375968933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5860440135002136},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.5770776271820068},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5198788642883301},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4830354154109955},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4498426914215088},{"id":"https://openalex.org/C2986834420","wikidata":"https://www.wikidata.org/wiki/Q5932254","display_name":"Age groups","level":2,"score":0.44488200545310974},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4429628551006317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44229137897491455},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.16471371054649353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14484402537345886},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco47968.2020.9287219","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W94242203","https://openalex.org/W254879503","https://openalex.org/W1905153633","https://openalex.org/W1997566808","https://openalex.org/W2009088607","https://openalex.org/W2035830871","https://openalex.org/W2096061497","https://openalex.org/W2096733369","https://openalex.org/W2103077782","https://openalex.org/W2105026179","https://openalex.org/W2106488920","https://openalex.org/W2121939926","https://openalex.org/W2137802466","https://openalex.org/W2163626514","https://openalex.org/W2170854557","https://openalex.org/W2239239723","https://openalex.org/W2267647074","https://openalex.org/W2401231614","https://openalex.org/W2440214111","https://openalex.org/W2510725918","https://openalex.org/W2570835027","https://openalex.org/W2592232824","https://openalex.org/W2725329413","https://openalex.org/W2747146256","https://openalex.org/W2750067310","https://openalex.org/W2890680318","https://openalex.org/W2901900434","https://openalex.org/W2915095025","https://openalex.org/W2962712577","https://openalex.org/W2963839617","https://openalex.org/W2964137095","https://openalex.org/W2979639244","https://openalex.org/W2979896090","https://openalex.org/W3009247505","https://openalex.org/W3035232573","https://openalex.org/W3099206234","https://openalex.org/W4285719527","https://openalex.org/W6609576614","https://openalex.org/W6680136119","https://openalex.org/W6693436464","https://openalex.org/W6713132643","https://openalex.org/W6725236325","https://openalex.org/W6754610156"],"related_works":["https://openalex.org/W3121380072","https://openalex.org/W2058403539","https://openalex.org/W2942793592","https://openalex.org/W2333615638","https://openalex.org/W2602311653","https://openalex.org/W2964230772","https://openalex.org/W2768231286","https://openalex.org/W2409976527","https://openalex.org/W2492471733","https://openalex.org/W626576356"],"abstract_inverted_index":{"Due":[0],"to":[1,34,45,157],"advances":[2],"in":[3,16,132],"deep":[4],"learning":[5],"and":[6,67,74,88,93,100,113,117,125],"convolutional":[7],"neural":[8],"networks":[9],"(CNNs)":[10],"there":[11,128],"has":[12],"been":[13],"significant":[14],"progress":[15],"the":[17,98,162],"field":[18],"of":[19,115],"visual":[20],"age":[21,37,64,83,122,141],"estimation":[22,38,65,84,123,142],"from":[23],"face":[24,105],"images":[25,106],"over":[26],"recent":[27],"years.":[28],"While":[29],"today's":[30],"models":[31,66,164],"are":[32,129],"able":[33],"achieve":[35],"considerable":[36],"accuracy,":[39],"their":[40,69,95],"behaviour,":[41],"especially":[42],"with":[43],"respect":[44],"specific":[46],"demographic":[47],"groups":[48],"is":[49,144],"still":[50],"not":[51,155],"well":[52],"understood.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57],"take":[58],"a":[59],"deeper":[60],"look":[61],"at":[62],"CNN-based":[63],"analyze":[68,94],"performance":[70,96,133],"across":[71,134,165],"different":[72,166],"race":[73,116,153],"gender":[75,112],"groups.":[76],"We":[77,103,119],"use":[78],"two":[79],"publicly":[80],"available":[81],"off-the-shelf":[82],"models,":[85],"i.e.,":[86],"FaceNet":[87],"WideResNet,":[89],"for":[90,147,150],"our":[91,137],"study":[92],"on":[97,110,161],"UTKFace":[99],"APPA-REAL":[101],"datasets.":[102,168],"partition":[104],"into":[107],"sub-groups":[108],"based":[109],"race,":[111],"combinations":[114],"gender.":[118],"then":[120],"compare":[121],"results":[124,138],"find":[126],"that":[127,140],"noticeable":[130],"differences":[131],"demographics.":[135],"Specifically,":[136],"show":[139],"accuracy":[143],"consistently":[145],"higher":[146],"men":[148],"than":[149],"women,":[151],"while":[152],"does":[154],"appear":[156],"have":[158],"consistent":[159],"effects":[160],"tested":[163],"test":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}