{"id":"https://openalex.org/W4392411854","doi":"https://doi.org/10.1109/ijcb57857.2023.10448893","title":"Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting","display_name":"Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting","publication_year":2023,"publication_date":"2023-09-25","ids":{"openalex":"https://openalex.org/W4392411854","doi":"https://doi.org/10.1109/ijcb57857.2023.10448893"},"language":"en","primary_location":{"id":"doi:10.1109/ijcb57857.2023.10448893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb57857.2023.10448893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5053160687","display_name":"Sahar Rahimi Malakshan","orcid":"https://orcid.org/0000-0002-6211-6039"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sahar Rahimi Malakshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087728270","display_name":"Mohammad Saeed Ebrahimi Saadabadi","orcid":"https://orcid.org/0000-0003-4112-626X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Saeed Ebrahimi Saadabadi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052406430","display_name":"Nima Najafzadeh","orcid":"https://orcid.org/0000-0002-8027-2342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nima Najafzadeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021852735","display_name":"Nasser M. Nasrabadi","orcid":"https://orcid.org/0000-0001-8730-627X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nasser M. Nasrabadi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5053160687"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3516,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6175999,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9983000159263611,"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":0.9980999827384949,"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/boosting","display_name":"Boosting (machine learning)","score":0.8838968276977539},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.8077645897865295},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6758201718330383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6730151772499084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.671337366104126},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5726786851882935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5722202062606812},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4155166745185852},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06305015087127686},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.04864603281021118}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8838968276977539},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8077645897865295},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6758201718330383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6730151772499084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.671337366104126},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5726786851882935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5722202062606812},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4155166745185852},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06305015087127686},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.04864603281021118},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"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/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb57857.2023.10448893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb57857.2023.10448893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W345900524","https://openalex.org/W398859631","https://openalex.org/W1678356000","https://openalex.org/W1968969471","https://openalex.org/W2096733369","https://openalex.org/W2100805904","https://openalex.org/W2128073546","https://openalex.org/W2144172034","https://openalex.org/W2151802538","https://openalex.org/W2163808566","https://openalex.org/W2168046285","https://openalex.org/W2194775991","https://openalex.org/W2404498690","https://openalex.org/W2515770085","https://openalex.org/W2520774990","https://openalex.org/W2553007282","https://openalex.org/W2566079294","https://openalex.org/W2613763509","https://openalex.org/W2663800299","https://openalex.org/W2736633948","https://openalex.org/W2752828042","https://openalex.org/W2779066530","https://openalex.org/W2782162974","https://openalex.org/W2799138232","https://openalex.org/W2871667416","https://openalex.org/W2894629025","https://openalex.org/W2949007385","https://openalex.org/W2962898354","https://openalex.org/W2963314072","https://openalex.org/W2963351448","https://openalex.org/W2963466847","https://openalex.org/W2963516811","https://openalex.org/W2963814162","https://openalex.org/W2963839617","https://openalex.org/W2969985801","https://openalex.org/W2998236288","https://openalex.org/W2998469040","https://openalex.org/W3034302825","https://openalex.org/W3035693354","https://openalex.org/W3167584510","https://openalex.org/W3169129566","https://openalex.org/W3178357922","https://openalex.org/W3185537204","https://openalex.org/W3190626620","https://openalex.org/W4248437541","https://openalex.org/W4280585406","https://openalex.org/W4292787341","https://openalex.org/W4312402191","https://openalex.org/W4312615396","https://openalex.org/W4312995938","https://openalex.org/W4316924402","https://openalex.org/W4316925060","https://openalex.org/W4318825117","https://openalex.org/W4319299736","https://openalex.org/W4319301109","https://openalex.org/W4319336174","https://openalex.org/W4398186455","https://openalex.org/W6638046521","https://openalex.org/W6676769703","https://openalex.org/W6679154154","https://openalex.org/W6681239517","https://openalex.org/W6682365079","https://openalex.org/W6730160095","https://openalex.org/W6744072679","https://openalex.org/W6750523955","https://openalex.org/W6766773798","https://openalex.org/W6799764170"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2125652721","https://openalex.org/W2127804977","https://openalex.org/W1540371141","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2040545019","https://openalex.org/W1549363203"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"have":[4],"achieved":[5],"remarkable":[6],"success":[7],"in":[8,156,162],"face":[9],"recognition":[10],"(FR),":[11],"partly":[12],"due":[13],"to":[14,58,74,105],"the":[15,20,54,65,76,82,88,100,112,115,121,125,131,137,159,166],"abundant":[16],"data":[17],"availability.":[18],"However,":[19],"current":[21,141],"training":[22,126],"benchmarks":[23],"exhibit":[24],"an":[25],"imbalanced":[26],"quality":[27],"distribution;":[28],"most":[29],"images":[30],"are":[31,45,91],"of":[32,78,87,96,102,133,140],"high":[33],"quality.":[34],"This":[35],"poses":[36],"issues":[37],"for":[38,119],"generalization":[39],"on":[40,114,136,165],"hard":[41],"samples":[42,80],"since":[43],"they":[44],"underrepresented":[46],"during":[47],"training.":[48],"In":[49],"this":[50,61],"work,":[51],"we":[52,68,128],"employ":[53],"multi-model":[55],"boosting":[56],"technique":[57],"deal":[59],"with":[60,158],"issue.":[62],"Inspired":[63],"by":[64],"well-known":[66],"AdaBoost,":[67],"propose":[69],"a":[70,106],"sample-level":[71],"weighting":[72],"approach":[73],"incorporate":[75],"importance":[77],"different":[79],"into":[81,124],"FR":[83],"loss.":[84],"Individual":[85],"models":[86,103],"proposed":[89,151],"framework":[90],"experts":[92],"at":[93],"distinct":[94],"levels":[95],"sample":[97,122,134],"hardness.":[98],"Therefore,":[99],"combination":[101],"leads":[104],"robust":[107],"feature":[108],"extractor":[109],"without":[110],"losing":[111],"discriminability":[113],"easy":[116],"samples.":[117],"Also,":[118],"incorporating":[120],"hardness":[123],"criterion,":[127],"analytically":[129],"show":[130],"effect":[132],"mining":[135],"important":[138],"aspects":[139],"angular":[142],"margin":[143,147],"loss":[144],"functions,":[145],"i.e.,":[146],"and":[148,174],"scale.":[149],"The":[150],"method":[152],"shows":[153],"superior":[154],"performance":[155],"comparison":[157],"state-of-the-art":[160],"algorithms":[161],"extensive":[163],"experiments":[164],"CFP-FP,":[167],"LFW,":[168],"CPLFW,":[169],"CALFW,":[170],"AgeDB,":[171],"TinyFace,":[172],"IJB-B,":[173],"IJB-C":[175],"evaluation":[176],"datasets.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}
