{"id":"https://openalex.org/W4414404706","doi":"https://doi.org/10.1109/tmm.2025.3613164","title":"RUL: Region Uncertainty Learning for Robust Face Recognition","display_name":"RUL: Region Uncertainty Learning for Robust Face Recognition","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414404706","doi":"https://doi.org/10.1109/tmm.2025.3613164"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2025.3613164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3613164","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-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/A5074679547","display_name":"Weiming Xiong","orcid":"https://orcid.org/0000-0002-2904-0781"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Xiong","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-2904-0781","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036232396","display_name":"Mingyang Zhong","orcid":"https://orcid.org/0000-0002-0150-8020"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyang Zhong","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-0150-8020","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047051147","display_name":"Shenglin Li","orcid":"https://orcid.org/0009-0003-0970-5760"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenglin Li","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0003-0970-5760","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048294072","display_name":"Guojun Huang","orcid":"https://orcid.org/0000-0002-4519-7068"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guojun Huang","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-4519-7068","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053928029","display_name":"Libo Zhang","orcid":"https://orcid.org/0000-0001-5992-0790"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libo Zhang","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-5992-0790","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22204631,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"9033","last_page":"9043"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9922000169754028,"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":0.9922000169754028,"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":0.9847000241279602,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9020000100135803,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7290999889373779},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5835000276565552},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4984000027179718},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.48829999566078186},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4733000099658966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46209999918937683},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4587000012397766},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4417000114917755},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.39739999175071716},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.39640000462532043}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7290999889373779},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7039999961853027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6230999827384949},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5835000276565552},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4984000027179718},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49720001220703125},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.48829999566078186},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3962000012397766},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.37290000915527344},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C2778865114","wikidata":"https://www.wikidata.org/wiki/Q7882489","display_name":"Uncertain data","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C176147448","wikidata":"https://www.wikidata.org/wiki/Q1889114","display_name":"Sensitivity analysis","level":3,"score":0.27639999985694885},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2025.3613164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3613164","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G144365148","display_name":null,"funder_award_id":"SWU-KT25001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3413195688","display_name":null,"funder_award_id":"62106205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7313470017","display_name":null,"funder_award_id":"62176220","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2096733369","https://openalex.org/W2304387544","https://openalex.org/W2404498690","https://openalex.org/W2609575245","https://openalex.org/W2663800299","https://openalex.org/W2963797156","https://openalex.org/W2964201641","https://openalex.org/W2969985801","https://openalex.org/W2984006054","https://openalex.org/W2984177071","https://openalex.org/W3000557356","https://openalex.org/W3035376925","https://openalex.org/W3093367965","https://openalex.org/W3160687038","https://openalex.org/W3162474807","https://openalex.org/W3169129566","https://openalex.org/W3183048323","https://openalex.org/W3191841357","https://openalex.org/W3195406476","https://openalex.org/W3197461641","https://openalex.org/W3208637248","https://openalex.org/W3210061328","https://openalex.org/W3217228024","https://openalex.org/W4205830784","https://openalex.org/W4210469523","https://openalex.org/W4225679478","https://openalex.org/W4226139462","https://openalex.org/W4287825176","https://openalex.org/W4308616028","https://openalex.org/W4308737645","https://openalex.org/W4312402191","https://openalex.org/W4313147265","https://openalex.org/W4317702595","https://openalex.org/W4323338370","https://openalex.org/W4372341649","https://openalex.org/W4386056879","https://openalex.org/W4386766812","https://openalex.org/W4386857888","https://openalex.org/W4387398386","https://openalex.org/W4390769786","https://openalex.org/W4391305588","https://openalex.org/W4402704506"],"related_works":[],"abstract_inverted_index":{"Data":[0,79],"uncertainty":[1,7,54,207,243],"refers":[2],"to":[3,69,91,118,158,194,234,241],"the":[4,16,37,46,49,53,102,108,133,146,150,155,160,171,206,211,237,242,250,261,271],"degree":[5],"of":[6,25,55,137,170,208,244,252,263,273],"in":[8,19,48,216,275],"model":[9,17,212],"predictions":[10],"caused":[11],"by":[12],"data":[13,71,93],"variability,":[14],"challenging":[15],"robustness":[18,215],"multimedia":[20],"applications.":[21],"The":[22],"increased":[23],"variability":[24],"uncontrolled":[26,217],"face":[27,59,85,222,256],"images,":[28],"such":[29,76],"as":[30,77,87],"mask":[31],"occlusion":[32],"and":[33,40,107,144,154,179,184,214,232,258],"image":[34,86],"blur,":[35],"increases":[36],"intra-class":[38],"differences":[39],"inter-class":[41],"similarities.":[42],"As":[43],"a":[44,88,124,167],"result,":[45],"ambiguity":[47],"learned":[50],"features":[51],"exacerbates":[52],"sample-to-class":[56,161],"membership.":[57,162,198,245],"Traditional":[58],"recognition":[60],"models":[61,66,97],"are":[62],"deterministic":[63],"point":[64],"embedding":[65,74],"that":[67],"fail":[68],"measure":[70,92,159],"uncertainty.":[72,94],"Probabilistic":[73],"models,":[75],"advanced":[78],"Uncertainty":[80,127],"Learning":[81,128],"(DUL),":[82],"represent":[83,196],"each":[84,138,141],"Gaussian":[89,135],"distribution":[90,103,136,152],"However,":[95],"these":[96],"perform":[98],"random":[99],"sampling":[100],"from":[101],"once":[104],"per":[105],"training,":[106],"sampled":[109],"points":[110],"may":[111],"fall":[112],"into":[113],"different":[114],"class":[115,156],"regions,":[116],"leading":[117],"training":[119,142],"oscillation.":[120],"Therefore,":[121],"we":[122,224,247,268],"propose":[123,225],"robust":[125,221],"Region":[126],"(RUL)":[129],"method,":[130],"which":[131],"adopts":[132],"entire":[134],"sample":[139,151],"during":[140],"epoch,":[143],"estimates":[145,176,189],"region":[147,153,157,264],"relations":[148,178],"between":[149],"In":[163,186,266],"fact,":[164],"DUL":[165,175],"is":[166],"special":[168],"case":[169],"proposed":[172],"RUL.":[173],"Specifically,":[174],"point-with-region":[177],"only":[180],"represents":[181,205],"absolute":[182],"membership":[183,202],"non-membership.":[185],"contrast,":[187],"RUL":[188,253,274],"region-with-region":[190],"relations,":[191],"enabling":[192],"it":[193],"additionally":[195],"incomplete":[197],"This":[199],"more":[200],"comprehensive":[201],"measurement":[203],"fully":[204],"membership,":[209],"enhancing":[210],"performance":[213],"scenes.":[218],"Furthermore,":[219],"for":[220],"recognition,":[223],"two":[226],"RUL-based":[227],"angular":[228],"margin":[229],"losses,":[230],"AngleFace":[231],"RegionFace,":[233],"adaptively":[235],"adjust":[236],"learning":[238],"weights":[239],"according":[240],"Finally,":[246],"comprehensively":[248],"evaluate":[249],"effectiveness":[251],"on":[254],"various":[255],"datasets,":[257],"profoundly":[259],"analyze":[260],"role":[262],"relations.":[265],"future,":[267],"will":[269],"explore":[270],"applicability":[272],"other":[276],"tasks.":[277]},"counts_by_year":[],"updated_date":"2025-12-19T00:32:22.182498","created_date":"2025-10-10T00:00:00"}