{"id":"https://openalex.org/W3119687891","doi":"https://doi.org/10.1109/tip.2020.3048632","title":"SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition","display_name":"SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3119687891","doi":"https://doi.org/10.1109/tip.2020.3048632","mag":"3119687891","pmid":"https://pubmed.ncbi.nlm.nih.gov/33417553"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3048632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3048632","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.12010","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028920827","display_name":"Yaoyao Zhong","orcid":"https://orcid.org/0000-0002-2671-9350"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoyao Zhong","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2671-9350","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025452586","display_name":"Weihong Deng","orcid":"https://orcid.org/0000-0001-5952-6996"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihong Deng","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5952-6996","affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063224243","display_name":"Jiani Hu","orcid":"https://orcid.org/0000-0002-2928-3169"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiani Hu","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025374137","display_name":"Dongyue Zhao","orcid":"https://orcid.org/0000-0002-0135-0614"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongyue Zhao","raw_affiliation_strings":["Canon Information Technology (Beijing) Company Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Canon Information Technology (Beijing) Company Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061256224","display_name":"Xian Li","orcid":"https://orcid.org/0000-0002-1509-9328"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xian Li","raw_affiliation_strings":["Canon Information Technology (Beijing) Company Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Canon Information Technology (Beijing) Company Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013433372","display_name":"Dongchao Wen","orcid":"https://orcid.org/0000-0001-7311-1842"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongchao Wen","raw_affiliation_strings":["Canon Information Technology (Beijing) Company Ltd., Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7311-1842","affiliations":[{"raw_affiliation_string":"Canon Information Technology (Beijing) Company Ltd., Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.4129,"has_fulltext":false,"cited_by_count":126,"citation_normalized_percentile":{"value":0.98635726,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"2587","last_page":"2598"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9976000189781189,"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":0.9958999752998352,"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/hypersphere","display_name":"Hypersphere","score":0.9525372982025146},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8521714806556702},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.8026565313339233},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7638565301895142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6753467321395874},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5951984524726868},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5858103036880493},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5729185342788696},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5084294676780701},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45699048042297363},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4269234836101532},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32898885011672974},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19855797290802002}],"concepts":[{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.9525372982025146},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8521714806556702},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.8026565313339233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7638565301895142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6753467321395874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5951984524726868},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5858103036880493},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5729185342788696},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5084294676780701},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45699048042297363},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4269234836101532},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32898885011672974},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19855797290802002},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3048632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3048632","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:33417553","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33417553","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2205.12010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.12010","pdf_url":"https://arxiv.org/pdf/2205.12010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.12010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.12010","pdf_url":"https://arxiv.org/pdf/2205.12010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W1505701726","https://openalex.org/W1509966554","https://openalex.org/W1703179648","https://openalex.org/W1782590233","https://openalex.org/W1836465849","https://openalex.org/W1949778830","https://openalex.org/W2019464758","https://openalex.org/W2024922353","https://openalex.org/W2095705004","https://openalex.org/W2096733369","https://openalex.org/W2111440402","https://openalex.org/W2144172034","https://openalex.org/W2145287260","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2325939864","https://openalex.org/W2404498690","https://openalex.org/W2515770085","https://openalex.org/W2520774990","https://openalex.org/W2555897561","https://openalex.org/W2557864411","https://openalex.org/W2600537992","https://openalex.org/W2609575245","https://openalex.org/W2663800299","https://openalex.org/W2737608545","https://openalex.org/W2752828042","https://openalex.org/W2781292787","https://openalex.org/W2796238763","https://openalex.org/W2871667416","https://openalex.org/W2882991827","https://openalex.org/W2884928667","https://openalex.org/W2887500939","https://openalex.org/W2891950293","https://openalex.org/W2949007385","https://openalex.org/W2949117887","https://openalex.org/W2952309299","https://openalex.org/W2955488837","https://openalex.org/W2962895364","https://openalex.org/W2962898354","https://openalex.org/W2962922273","https://openalex.org/W2962950337","https://openalex.org/W2963224870","https://openalex.org/W2963466847","https://openalex.org/W2963559058","https://openalex.org/W2963656735","https://openalex.org/W2963671154","https://openalex.org/W2963814162","https://openalex.org/W2963839617","https://openalex.org/W2964254778","https://openalex.org/W2964605770","https://openalex.org/W2967637014","https://openalex.org/W2969985801","https://openalex.org/W2970084480","https://openalex.org/W2974328324","https://openalex.org/W2985817549","https://openalex.org/W2998236288","https://openalex.org/W2998469040","https://openalex.org/W3035693354","https://openalex.org/W3099206234","https://openalex.org/W3101227480","https://openalex.org/W4293478066","https://openalex.org/W4300167309","https://openalex.org/W6630046087","https://openalex.org/W6630649318","https://openalex.org/W6637320534","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6676885845","https://openalex.org/W6681239517","https://openalex.org/W6686509673","https://openalex.org/W6700903540","https://openalex.org/W6730323794","https://openalex.org/W6735013348","https://openalex.org/W6744072679","https://openalex.org/W6750318685","https://openalex.org/W6751593755","https://openalex.org/W6753410490","https://openalex.org/W6766258606","https://openalex.org/W6770761745","https://openalex.org/W6842019321"],"related_works":["https://openalex.org/W2099702253","https://openalex.org/W2059484267","https://openalex.org/W1970780628","https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W2069017679","https://openalex.org/W2133653616","https://openalex.org/W2921850681","https://openalex.org/W3009056573","https://openalex.org/W4297676672"],"abstract_inverted_index":{"Deep":[0],"face":[1,160,178],"recognition":[2,161,179],"has":[3],"achieved":[4],"great":[5],"success":[6],"due":[7],"to":[8,21,76,130,151],"large-scale":[9],"training":[10,45,60,125],"databases":[11],"and":[12,30,66,80,96,120,148,155,171,174,185],"rapidly":[13],"developing":[14],"loss":[15,85,90],"functions.":[16],"The":[17,113],"existing":[18],"algorithms":[19],"devote":[20],"realizing":[22],"an":[23],"ideal":[24],"idea:":[25],"minimizing":[26],"the":[27,32,57,118,142,152,190],"intra-class":[28,65,95,119,143],"distance":[29],"maximizing":[31],"inter-class":[33,67,97,121],"distance.":[34],"However,":[35],"they":[36],"may":[37],"neglect":[38],"that":[39,64,124],"there":[40],"are":[41,104],"also":[42],"low":[43],"quality":[44],"images":[46],"which":[47,103],"should":[48],"not":[49],"be":[50,70,128],"optimized":[51,71,129],"in":[52,72],"this":[53],"strict":[54],"way.":[55],"Considering":[56],"imperfection":[58],"of":[59,165,192],"databases,":[61,173,187],"we":[62],"propose":[63,82],"objectives":[68],"can":[69,127,135],"a":[73,83,100,137],"moderate":[74],"way":[75],"mitigate":[77],"overfitting":[78,150],"problem,":[79],"further":[81],"novel":[84],"function,":[86],"named":[87],"sigmoid-constrained":[88],"hypersphere":[89,101],"(SFace).":[91],"Specifically,":[92],"SFace":[93,134],"imposes":[94],"constraints":[98],"on":[99,168,176],"manifold,":[102],"controlled":[105],"by":[106],"two":[107],"sigmoid":[108,114],"gradient":[109],"re-scale":[110,117],"functions":[111],"respectively.":[112],"curves":[115],"precisely":[116],"gradients":[122],"so":[123],"samples":[126],"some":[131],"degree.":[132],"Therefore,":[133],"make":[136],"better":[138],"balance":[139],"between":[140],"decreasing":[141],"distances":[144],"for":[145],"clean":[146],"examples":[147],"preventing":[149],"label":[153],"noise,":[154],"contributes":[156],"more":[157],"robust":[158],"deep":[159],"models.":[162],"Extensive":[163],"experiments":[164],"models":[166],"trained":[167],"CASIA-WebFace,":[169],"VGGFace2,":[170],"MS-Celeb-1M":[172],"evaluated":[175],"several":[177],"benchmarks,":[180],"such":[181],"as":[182],"LFW,":[183],"MegaFace":[184],"IJB-C":[186],"have":[188],"demonstrated":[189],"superiority":[191],"SFace.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":10},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2025-10-10T00:00:00"}
