{"id":"https://openalex.org/W3178510125","doi":"https://doi.org/10.1109/access.2021.3095666","title":"Deep Learning for Vein Biometric Recognition on a Smartphone","display_name":"Deep Learning for Vein Biometric Recognition on a Smartphone","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3178510125","doi":"https://doi.org/10.1109/access.2021.3095666","mag":"3178510125"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3095666","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3095666","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09477578.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09477578.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044937303","display_name":"Raul Garcia-Martin","orcid":"https://orcid.org/0000-0001-5319-2016"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Raul Garcia-Martin","raw_affiliation_strings":["Electronic Technology Department, University Carlos III of Madrid, Legan\u00e9s, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Technology Department, University Carlos III of Madrid, Legan\u00e9s, Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009257213","display_name":"Ra\u00fal S\u00e1nchez-Reillo","orcid":"https://orcid.org/0000-0003-4239-985X"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Raul Sanchez-Reillo","raw_affiliation_strings":["Electronic Technology Department, University Carlos III of Madrid, Legan\u00e9s, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Technology Department, University Carlos III of Madrid, Legan\u00e9s, Spain","institution_ids":["https://openalex.org/I50357001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I50357001"],"apc_list":{"value":1750,"currency":"USD","value_usd":1750},"apc_paid":{"value":1750,"currency":"USD","value_usd":1750},"fwci":1.6886,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.83907798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"9","issue":null,"first_page":"98812","last_page":"98832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.988099992275238,"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":0.988099992275238,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9699000120162964,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8373375535011292},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.7756351828575134},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7566068172454834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6996669769287109},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.6478264927864075},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6469075679779053},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5346020460128784},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5062493681907654},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4989941120147705},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4247962236404419},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4109788239002228},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.407793253660202}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8373375535011292},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7756351828575134},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7566068172454834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6996669769287109},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.6478264927864075},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6469075679779053},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5346020460128784},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5062493681907654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4989941120147705},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4247962236404419},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4109788239002228},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.407793253660202},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3095666","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3095666","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09477578.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7cf47a0b904d4bb7b53a5f800c553d88","is_oa":true,"landing_page_url":"https://doaj.org/article/7cf47a0b904d4bb7b53a5f800c553d88","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 98812-98832 (2021)","raw_type":"article"},{"id":"pmh:oai:e-archivo.uc3m.es:10016/33798","is_oa":true,"landing_page_url":"http://hdl.handle.net/10016/33798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400817","display_name":"e-Archivo (Carlos III University of Madrid)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I50357001","host_organization_name":"Universidad Carlos III de Madrid","host_organization_lineage":["https://openalex.org/I50357001"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3095666","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3095666","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09477578.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3178510125.pdf","grobid_xml":"https://content.openalex.org/works/W3178510125.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1909312576","https://openalex.org/W2026206312","https://openalex.org/W2037829978","https://openalex.org/W2042087214","https://openalex.org/W2054432162","https://openalex.org/W2058333183","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2549593664","https://openalex.org/W2600360414","https://openalex.org/W2626053630","https://openalex.org/W2884114979","https://openalex.org/W2938788221","https://openalex.org/W2945197573","https://openalex.org/W2963446712","https://openalex.org/W2982703821","https://openalex.org/W3005370840","https://openalex.org/W3010583178","https://openalex.org/W3021558250","https://openalex.org/W3033909149","https://openalex.org/W3049342961","https://openalex.org/W3088350551","https://openalex.org/W3112970247","https://openalex.org/W6664581316"],"related_works":["https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3176438653","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W3141979996","https://openalex.org/W2253429366","https://openalex.org/W3127975138"],"abstract_inverted_index":{"The":[0,182,228],"ongoing":[1],"COVID-19":[2],"pandemic":[3],"has":[4,80,144,164,185],"pointed":[5],"out,":[6],"even":[7],"more,":[8],"the":[9,43,86,189,194],"important":[10],"need":[11],"for":[12,75,85,243],"hygiene":[13],"contactless":[14,51],"biometric":[15,201],"recognition":[16,54],"systems.":[17],"Vein-based":[18],"devices":[19],"are":[20,129],"great":[21],"non-contact":[22],"options":[23],"although":[24],"they":[25],"have":[26,170],"not":[27],"been":[28,81,145,165,171,186],"entirely":[29],"well-integrated":[30],"in":[31,37,89,237],"daily":[32],"life.":[33],"In":[34,156],"this":[35,157],"work,":[36],"an":[38],"attempt":[39],"to":[40,42,123,204],"contribute":[41],"research":[44,91],"and":[45,83,137,180,193,208,220,234],"development":[46],"of":[47,149,206,212],"these":[48],"devices,":[49],"a":[50,57,90,94,98,114,132,141,150,159,215],"wrist":[52,175],"vein":[53,176],"system":[55],"with":[56,131,147,214],"real-life":[58,244],"application":[59],"is":[60,97],"revealed.":[61],"A":[62],"Transfer":[63],"Learning":[64,100,135],"(TL)":[65],"method,":[66],"based":[67],"on":[68,93,173,188,218],"different":[69,174],"Deep":[70,99],"Convolutional":[71,119],"Neural":[72,120],"Networks":[73],"architectures,":[74],"Vascular":[76],"Biometric":[77],"Recognition":[78],"(VBR),":[79],"designed":[82],"tested,":[84],"first":[87],"time":[88],"approach,":[92],"smartphone.":[95],"TL":[96],"(DL)":[101],"technique":[102],"that":[103,127,143],"could":[104],"be":[105],"divided":[106],"into":[107],"networks":[108,169],"as":[109],"feature":[110,160],"extractor,":[111],"i.e.,":[112,139],"using":[113],"pre-trained":[115,151],"(different":[116,152],"large-scale":[117,153],"dataset)":[118,154],"Network":[121],"(CNN)":[122],"obtain":[124],"unique":[125],"features":[126],"then,":[128],"classified":[130],"traditional":[133],"Machine":[134],"algorithm,":[136],"fine-tuning,":[138],"training":[140],"CNN":[142],"initialized":[146],"weights":[148],"CNN.":[155],"study,":[158],"extractor":[161],"base":[162],"method":[163],"employed.":[166],"Several":[167],"architecture":[168],"tested":[172],"datasets:":[177],"UC3M-CV1,":[178],"UC3M-CV2,":[179,219],"PUT.":[181],"DL":[183,232],"model":[184],"integrated":[187],"Xiaomi\u00a9":[190,195],"Pocophone":[191],"F1":[192],"Mi":[196],"8":[197],"smartphones":[198],"obtaining":[199],"high":[200,231],"performance,":[202],"up":[203],"98%":[205],"accuracy":[207],"less":[209,224],"than":[210,225],"0.4%":[211],"EER":[213],"50\u201350%":[216],"train-test":[217],"fast":[221],"identification/verification":[222],"time,":[223],"300":[226],"milliseconds.":[227],"results":[229],"infer,":[230],"performance":[233],"integration":[235],"reachable":[236],"VBR":[238],"without":[239],"direct":[240],"user-device":[241],"contact,":[242],"applications":[245],"nowadays.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}