{"id":"https://openalex.org/W3186831428","doi":"https://doi.org/10.1109/ijcb52358.2021.9484388","title":"Contrastive Uncertainty Learning for Iris Recognition with Insufficient Labeled Samples","display_name":"Contrastive Uncertainty Learning for Iris Recognition with Insufficient Labeled Samples","publication_year":2021,"publication_date":"2021-07-20","ids":{"openalex":"https://openalex.org/W3186831428","doi":"https://doi.org/10.1109/ijcb52358.2021.9484388","mag":"3186831428"},"language":"en","primary_location":{"id":"doi:10.1109/ijcb52358.2021.9484388","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb52358.2021.9484388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5048192467","display_name":"Jianze Wei","orcid":"https://orcid.org/0000-0002-5774-6765"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianze Wei","raw_affiliation_strings":["CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112749024","display_name":"Ran He","orcid":"https://orcid.org/0000-0002-3807-991X"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran He","raw_affiliation_strings":["CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055505703","display_name":"Zhenan Sun","orcid":"https://orcid.org/0000-0003-4029-9935"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenan Sun","raw_affiliation_strings":["CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048192467"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210100255","https://openalex.org/I4210112150","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.4571,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6096687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9995999932289124,"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":0.9606000185012817,"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/T11448","display_name":"Face recognition and analysis","score":0.9573000073432922,"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/discriminative-model","display_name":"Discriminative model","score":0.8397049903869629},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7502951622009277},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6954354047775269},{"id":"https://openalex.org/keywords/iris-recognition","display_name":"Iris recognition","score":0.6857905983924866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6515683531761169},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5894032120704651},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.575326144695282},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4845770597457886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4690896272659302},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4681221544742584},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46325573325157166},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4350922107696533},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42140066623687744},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.30113181471824646},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.06912842392921448}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8397049903869629},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7502951622009277},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6954354047775269},{"id":"https://openalex.org/C112356035","wikidata":"https://www.wikidata.org/wiki/Q1672722","display_name":"Iris recognition","level":3,"score":0.6857905983924866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6515683531761169},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5894032120704651},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.575326144695282},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4845770597457886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4690896272659302},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4681221544742584},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46325573325157166},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4350922107696533},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42140066623687744},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.30113181471824646},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.06912842392921448},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb52358.2021.9484388","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb52358.2021.9484388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W2020754786","https://openalex.org/W2100603595","https://openalex.org/W2102796633","https://openalex.org/W2115718877","https://openalex.org/W2138621090","https://openalex.org/W2149999708","https://openalex.org/W2395240779","https://openalex.org/W2520742745","https://openalex.org/W2552233335","https://openalex.org/W2618530766","https://openalex.org/W2780564062","https://openalex.org/W2785325870","https://openalex.org/W2799891027","https://openalex.org/W2810075754","https://openalex.org/W2842511635","https://openalex.org/W2883725317","https://openalex.org/W2887997457","https://openalex.org/W2889960480","https://openalex.org/W2927438889","https://openalex.org/W2944828972","https://openalex.org/W2948012107","https://openalex.org/W2951104886","https://openalex.org/W2962770929","https://openalex.org/W2963460857","https://openalex.org/W2969985801","https://openalex.org/W2971155163","https://openalex.org/W2990217661","https://openalex.org/W3005680577","https://openalex.org/W3034978746","https://openalex.org/W3035058308","https://openalex.org/W3035131939","https://openalex.org/W3035524453","https://openalex.org/W3083710176","https://openalex.org/W3091246867","https://openalex.org/W3108655343","https://openalex.org/W4297808394","https://openalex.org/W6674973497","https://openalex.org/W6681909610","https://openalex.org/W6712091502","https://openalex.org/W6729787662","https://openalex.org/W6747899497","https://openalex.org/W6752515464","https://openalex.org/W6753000030","https://openalex.org/W6754278344","https://openalex.org/W6762573206","https://openalex.org/W6763416564","https://openalex.org/W6763442200","https://openalex.org/W6770810224","https://openalex.org/W6774314701","https://openalex.org/W6844194202"],"related_works":["https://openalex.org/W2018223046","https://openalex.org/W2294693339","https://openalex.org/W1971649232","https://openalex.org/W2573408845","https://openalex.org/W2774310452","https://openalex.org/W231692020","https://openalex.org/W3092387234","https://openalex.org/W2024700913","https://openalex.org/W3119773509","https://openalex.org/W3208297503"],"abstract_inverted_index":{"Cross-database":[0],"recognition":[1,11,32,44,177],"is":[2],"still":[3],"an":[4,9],"unavoidable":[5],"challenge":[6],"when":[7],"deploying":[8],"iris":[10,31,102,176],"system":[12],"to":[13,41,76,99,140,153],"a":[14,22,78,96],"new":[15,38],"environment.":[16],"In":[17,104],"the":[18,27,43,53,63,85,90,101,105,108,117,122,129,133,155,161,169,172],"paper,":[19],"we":[20,55],"present":[21],"compromise":[23],"problem":[24,39],"that":[25],"resembles":[26],"real-world":[28],"scenario,":[29],"named":[30],"with":[33,178],"insufficient":[34,179],"labeled":[35,180],"samples.":[36,181],"This":[37],"aims":[40],"improve":[42],"performance":[45],"by":[46,61],"utilizing":[47],"partially-or":[48],"un-labeled":[49],"data.":[50],"To":[51],"address":[52],"problem,":[54],"propose":[56],"Contrastive":[57],"Uncertainty":[58],"Learning":[59],"(CUL)":[60],"integrating":[62],"merits":[64],"of":[65,124,171],"uncertainty":[66],"learning":[67],"and":[68,80,94,111,119,144,159],"contrastive":[69,151],"self-supervised":[70],"learning.":[71],"CUL":[72,88,136,148,174],"makes":[73],"two":[74],"efforts":[75],"learn":[77],"discriminative":[79],"robust":[81],"feature":[82],"representation.":[83],"On":[84,132],"one":[86],"hand,":[87,135],"explores":[89],"uncertain":[91,125],"acquisition":[92,112,126],"factors":[93,113,127],"adopts":[95],"probabilistic":[97,106,138],"embedding":[98],"represent":[100],"image.":[103],"representation,":[107],"identity":[109,130],"information":[110],"are":[114],"disentangled":[115],"into":[116],"mean":[118],"variance,":[120],"avoiding":[121],"impact":[123],"on":[128],"information.":[131],"other":[134],"utilizes":[137],"embeddings":[139],"generate":[141],"virtual":[142],"positive":[143],"negative":[145],"pairs.":[146],"Then":[147],"builds":[149],"its":[150],"loss":[152],"group":[154],"similar":[156],"samples":[157,163],"closely":[158],"push":[160],"dissimilar":[162],"apart.":[164],"The":[165],"experimental":[166],"results":[167],"demonstrate":[168],"effectiveness":[170],"proposed":[173],"for":[175]},"counts_by_year":[{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
