{"id":"https://openalex.org/W4316924973","doi":"https://doi.org/10.1109/ijcb54206.2022.10007941","title":"Face Regions Impact Recognition Accuracy Differently Across Demographics","display_name":"Face Regions Impact Recognition Accuracy Differently Across Demographics","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4316924973","doi":"https://doi.org/10.1109/ijcb54206.2022.10007941"},"language":"en","primary_location":{"id":"doi:10.1109/ijcb54206.2022.10007941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb54206.2022.10007941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Joint Conference on Biometrics (IJCB)","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/A5061227377","display_name":"V\u00edtor Albiero","orcid":"https://orcid.org/0000-0002-8607-9914"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vitor Albiero","raw_affiliation_strings":["University of Notre Dame,Department of Computer Science and Engineering","Department of Computer Science and Engineering, University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Department of Computer Science and Engineering","institution_ids":["https://openalex.org/I107639228"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019673624","display_name":"Kevin W. Bowyer","orcid":"https://orcid.org/0000-0002-7562-4390"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin W. Bowyer","raw_affiliation_strings":["University of Notre Dame,Department of Computer Science and Engineering","Department of Computer Science and Engineering, University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame,Department of Computer Science and Engineering","institution_ids":["https://openalex.org/I107639228"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027859840","display_name":"Michael C. King","orcid":"https://orcid.org/0000-0002-3219-5477"},"institutions":[{"id":"https://openalex.org/I106959904","display_name":"Florida Institute of Technology","ror":"https://ror.org/04atsbb87","country_code":"US","type":"education","lineage":["https://openalex.org/I106959904"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael C. King","raw_affiliation_strings":["Florida Institute of Technology,Department of Computer Engineering and Sciences","Department of Computer Engineering and Sciences, Florida Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Institute of Technology,Department of Computer Engineering and Sciences","institution_ids":["https://openalex.org/I106959904"]},{"raw_affiliation_string":"Department of Computer Engineering and Sciences, Florida Institute of Technology","institution_ids":["https://openalex.org/I106959904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4061,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6127727,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9997000098228455,"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":0.9997000098228455,"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/T11118","display_name":"Evolutionary Psychology and Human Behavior","score":0.9940000176429749,"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/T11094","display_name":"Face Recognition and Perception","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.796513557434082},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.7480124831199646},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.661546528339386},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5960109233856201},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4869345426559448},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4803220331668854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46007993817329407},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.414995402097702},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.36848336458206177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3642829358577728},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2657107710838318},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.2351602017879486},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22426128387451172},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13031437993049622},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.07511267066001892}],"concepts":[{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.796513557434082},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.7480124831199646},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.661546528339386},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5960109233856201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4869345426559448},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4803220331668854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46007993817329407},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.414995402097702},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.36848336458206177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3642829358577728},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2657107710838318},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.2351602017879486},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22426128387451172},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13031437993049622},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.07511267066001892},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb54206.2022.10007941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb54206.2022.10007941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1978642336","https://openalex.org/W2004126537","https://openalex.org/W2099312605","https://openalex.org/W2117715370","https://openalex.org/W2118664399","https://openalex.org/W2121455381","https://openalex.org/W2194775991","https://openalex.org/W2223285751","https://openalex.org/W2279349648","https://openalex.org/W2569242799","https://openalex.org/W2747146256","https://openalex.org/W2886934227","https://openalex.org/W2912376035","https://openalex.org/W2962898354","https://openalex.org/W2963466847","https://openalex.org/W2969985801","https://openalex.org/W3009247505","https://openalex.org/W3010093963","https://openalex.org/W3010400237","https://openalex.org/W3021840842","https://openalex.org/W3034552680","https://openalex.org/W3035232573","https://openalex.org/W3118953353","https://openalex.org/W3175906877","https://openalex.org/W4206687860","https://openalex.org/W4221027031","https://openalex.org/W4233204037","https://openalex.org/W4246904949","https://openalex.org/W6761354291","https://openalex.org/W6782174909"],"related_works":["https://openalex.org/W3121380072","https://openalex.org/W2058403539","https://openalex.org/W2333615638","https://openalex.org/W2602311653","https://openalex.org/W2964230772","https://openalex.org/W2768231286","https://openalex.org/W2973958681","https://openalex.org/W2942793592","https://openalex.org/W2409976527","https://openalex.org/W2384651879"],"abstract_inverted_index":{"Variation":[0],"in":[1,24,58,119,135,157,181,216],"face":[2,82,116,132,154,174,214],"recognition":[3],"accuracy":[4,70,93,121,201],"across":[5,31,39,44,95,123,187,203],"demographic":[6,45,69,96,204],"groups":[7,97,205],"has":[8],"attracted":[9],"attention":[10],"from":[11],"news":[12],"media,":[13],"civil":[14],"liberties":[15],"advocates":[16],"and":[17,34,56],"academic":[18],"researchers.":[19],"The":[20],"problem":[21],"is":[22,170],"challenging,":[23],"that":[25,108,168,176,195],"both":[26],"the":[27,35,52,59,74,81,91,103,115,128,151,179,182,210,213],"impostor":[28,141,163,183],"distribution":[29,37,142],"(matches":[30,38],"different":[32,112],"people)":[33,41],"genuine":[36,185],"same":[40,92],"may":[42,117,206],"vary":[43],"groups.":[46],"Simple":[47],"answers":[48],"such":[49,84],"as":[50,85,98],"balancing":[51],"number":[53],"of":[54,80,114,212],"subjects":[55],"images":[57],"training":[60],"data":[61],"do":[62],"not":[63],"have":[64],"a":[65,139,161,196],"substantial":[66],"impact":[67],"on":[68,111],"disparities.":[71],"We":[72,106,165],"present":[73],"first":[75],"investigation":[76],"into":[77],"whether":[78],"parts":[79,113,211],"-":[83,89],"eyes,":[86],"nose,":[87],"mouth":[88],"show":[90,107,167],"differences":[94,122],"are":[99],"seen":[100],"with":[101],"matching":[102,109,133,155],"whole":[104],"face.":[105],"focused":[110],"result":[118],"opposite":[120],"demographics.":[124,191],"For":[125],"example,":[126],"using":[127,150],"eye":[129],"region":[130],"for":[131],"results":[134,156,193],"Caucasian":[136,147,158],"males":[137],"having":[138,160],"better":[140,162],"(lower":[143],"similarity":[144],"scores)":[145],"than":[146],"females,":[148],"but":[149],"nose":[152],"regionfor":[153],"females":[159],"distribution.":[164],"also":[166],"it":[169],"possible":[171],"to":[172,199,208],"select":[173],"region(s)":[175],"effectively":[177],"minimize":[178],"difference":[180],"or":[184],"distributions":[186],"at":[188],"least":[189],"some":[190],"Our":[192],"suggest":[194],"new":[197],"pathway":[198],"reducing":[200],"disparity":[202],"be":[207],"weight":[209],"differently":[215],"matching.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}