{"id":"https://openalex.org/W4225291199","doi":"https://doi.org/10.1145/3523111.3523112","title":"Towards Face Representation Learning Conditioned on the Soft Biometrics","display_name":"Towards Face Representation Learning Conditioned on the Soft Biometrics","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4225291199","doi":"https://doi.org/10.1145/3523111.3523112"},"language":"en","primary_location":{"id":"doi:10.1145/3523111.3523112","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523111.3523112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 the 5th International Conference on Machine Vision and Applications (ICMVA)","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/A5029530173","display_name":"JongWon Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"JongWon Hwang","raw_affiliation_strings":["Electrical and Electronic Engineering/Yonsei/Multimedia Security Lab, Yonsei University,Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Electronic Engineering/Yonsei/Multimedia Security Lab, Yonsei University,Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008881758","display_name":"Leslie Ching Ow Tiong","orcid":"https://orcid.org/0000-0003-3786-2117"},"institutions":[{"id":"https://openalex.org/I58716616","display_name":"Korea Institute of Science and Technology","ror":"https://ror.org/05kzfa883","country_code":"KR","type":"facility","lineage":["https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098","https://openalex.org/I58716616"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Leslie Ching Ow Tiong","raw_affiliation_strings":["Computational Science Research Center, Korea institute of Science and Technology, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Science Research Center, Korea institute of Science and Technology, South Korea","institution_ids":["https://openalex.org/I58716616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051093782","display_name":"Andrew Beng Jin Teoh","orcid":"https://orcid.org/0000-0001-5063-9484"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Andrew Beng Jin Teoh","raw_affiliation_strings":["Electrical and Electronic Engineering/Yonsei/Multimedia Security Lab, Yonsei University,Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Electronic Engineering/Yonsei/Multimedia Security Lab, Yonsei University,Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1015,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.35596989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10828","display_name":"Biometric Identification and Security","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9962000250816345,"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/biometrics","display_name":"Biometrics","score":0.9190489053726196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7702702283859253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6906532049179077},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6179373264312744},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6033860445022583},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5791823863983154},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5215006470680237},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4747738540172577},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47135013341903687},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46780019998550415},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4512346386909485},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44964492321014404},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4240576922893524},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4106537997722626},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35537827014923096}],"concepts":[{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.9190489053726196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7702702283859253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6906532049179077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6179373264312744},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6033860445022583},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5791823863983154},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5215006470680237},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4747738540172577},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47135013341903687},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46780019998550415},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4512346386909485},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44964492321014404},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4240576922893524},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4106537997722626},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35537827014923096},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3523111.3523112","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523111.3523112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 the 5th International Conference on Machine Vision and Applications (ICMVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1966572524","https://openalex.org/W1998808035","https://openalex.org/W2081696085","https://openalex.org/W2096733369","https://openalex.org/W2140609507","https://openalex.org/W2145287260","https://openalex.org/W2526403378","https://openalex.org/W2528333963","https://openalex.org/W2609296554","https://openalex.org/W2766818358","https://openalex.org/W2784874046","https://openalex.org/W2793708128","https://openalex.org/W2794104577","https://openalex.org/W2798978806","https://openalex.org/W2901178729","https://openalex.org/W2905396542","https://openalex.org/W2936185954","https://openalex.org/W2953857186","https://openalex.org/W2962898354","https://openalex.org/W3083599435","https://openalex.org/W4301045096","https://openalex.org/W6640775171","https://openalex.org/W6680902425","https://openalex.org/W6681239517","https://openalex.org/W6687483927","https://openalex.org/W6785611192","https://openalex.org/W6787662403"],"related_works":["https://openalex.org/W2076845124","https://openalex.org/W2183964146","https://openalex.org/W2379932303","https://openalex.org/W3147744369","https://openalex.org/W4241440711","https://openalex.org/W2062586268","https://openalex.org/W2019582947","https://openalex.org/W3212688212","https://openalex.org/W4300873085","https://openalex.org/W4300552992"],"abstract_inverted_index":{"Abstract:":[0],"In":[1],"this":[2],"paper,":[3],"we":[4,25,153],"present":[5],"a":[6,13,56,63,69,77,115,155],"method":[7,181],"to":[8,129,140,183],"leverage":[9],"soft":[10,28,81,107,151,171],"biometric":[11,29,39,82,108],"as":[12,36,114],"means":[14],"of":[15,62,118,134,143,179],"conditioning":[16,106],"biometrics":[17,172],"for":[18,40,157],"better":[19],"face":[20,44,85,124,136,145,166,185],"representation":[21,79,109],"learning.":[22],"By":[23],"conditioning,":[24],"meant":[26],"the":[27,51,96,111,119,123,131,135,141,144,174,177,184],"trait":[30],"(age,":[31],"gender,":[32],"etc.)":[33],"is":[34,48,127],"used":[35],"an":[37],"auxiliary":[38],"training":[41],"along":[42,149],"with":[43,150,170],"modality":[45,186],"while":[46],"it":[47],"absent":[49],"during":[50],"inference":[52],"stage.":[53],"We":[54],"propose":[55],"two-stream":[57,89],"deep":[58],"neural":[59,71],"network":[60,66,72,90],"consisting":[61],"multilayer":[64],"perceptron":[65],"(MLP)":[67],"and":[68,84,95,173],"convolutional":[70],"(CNN),":[73],"which":[74,126],"can":[75,91,98],"learn":[76],"feature":[78,120,137],"from":[80,101,110,122],"vectors":[83],"images,":[86],"respectively.":[87],"The":[88,104],"be":[92,99],"optimized":[93],"simultaneously":[94],"information":[97],"exploited":[100],"both":[102],"biometrics.":[103],"learned":[105,121],"MLP":[112],"serves":[113],"center":[116],"prototype":[117],"network,":[125],"beneficial":[128],"contract":[130],"intra-class":[132],"variation":[133],"representation.":[138],"Due":[139],"lacking":[142],"dataset":[146],"that":[147,168],"comes":[148],"biometrics,":[152],"construct":[154],"database":[156],"evaluation":[158],"purposes.":[159],"Extensive":[160],"experiments":[161],"are":[162],"performed":[163],"on":[164],"two":[165],"datasets":[167],"equip":[169],"results":[175],"show":[176],"superiority":[178],"our":[180],"compared":[182],"alone.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
