{"id":"https://openalex.org/W4205268597","doi":"https://doi.org/10.1109/bigdata52589.2021.9671708","title":"A study on the calibration of fingerprint classifiers","display_name":"A study on the calibration of fingerprint classifiers","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205268597","doi":"https://doi.org/10.1109/bigdata52589.2021.9671708"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671708","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082713397","display_name":"Daniel Peralta","orcid":"https://orcid.org/0000-0002-7544-8411"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Daniel Peralta","raw_affiliation_strings":["Department of Information Technology, IDLab, Ghent University - Imec, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, IDLab, Ghent University - Imec, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064085134","display_name":"Lin Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I4210139803","display_name":"VIB-UGent Center for Inflammation Research","ror":"https://ror.org/04q4ydz28","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210139803"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Lin Tang","raw_affiliation_strings":["Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200","https://openalex.org/I4210139803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037729581","display_name":"Maxim Lippeveld","orcid":"https://orcid.org/0000-0002-9527-7162"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I4210139803","display_name":"VIB-UGent Center for Inflammation Research","ror":"https://ror.org/04q4ydz28","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210139803"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Maxim Lippeveld","raw_affiliation_strings":["Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200","https://openalex.org/I4210139803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001585713","display_name":"Yvan Saeys","orcid":"https://orcid.org/0000-0002-0415-1506"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I4210139803","display_name":"VIB-UGent Center for Inflammation Research","ror":"https://ror.org/04q4ydz28","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210139803"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Yvan Saeys","raw_affiliation_strings":["Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200","https://openalex.org/I4210139803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"698","last_page":"704"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9818000197410583,"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/T13192","display_name":"Forensic Fingerprint Detection Methods","score":0.9688000082969666,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7162061929702759},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6645694375038147},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.6363303661346436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6353768706321716},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6182907819747925},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.6064527034759521},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5707175731658936},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5170705914497375},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4942157566547394},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.45813965797424316},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45230937004089355},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.429095983505249},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40900179743766785},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2347208559513092},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16659820079803467}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7162061929702759},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6645694375038147},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.6363303661346436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6353768706321716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6182907819747925},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.6064527034759521},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5707175731658936},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5170705914497375},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4942157566547394},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.45813965797424316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45230937004089355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.429095983505249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40900179743766785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2347208559513092},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16659820079803467},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671708","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:8734287","is_oa":false,"landing_page_url":"https://biblio.ugent.be/publication/8734287","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9781665439022","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6104088745","display_name":"An exploratory study of machine learning techniques for imaging flow cytometry","funder_award_id":"1SB9421N","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"},{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1540419678","https://openalex.org/W1599263113","https://openalex.org/W1618905105","https://openalex.org/W2012942264","https://openalex.org/W2052655256","https://openalex.org/W2098824882","https://openalex.org/W2151613430","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2584050167","https://openalex.org/W2603646780","https://openalex.org/W2609818166","https://openalex.org/W2625817666","https://openalex.org/W2626967530","https://openalex.org/W2890227554","https://openalex.org/W2898529688","https://openalex.org/W2912819642","https://openalex.org/W2919115771","https://openalex.org/W2967304386","https://openalex.org/W2971118045","https://openalex.org/W3005805562","https://openalex.org/W3010909809","https://openalex.org/W3035224318","https://openalex.org/W3108357504","https://openalex.org/W3111920840","https://openalex.org/W3150812189","https://openalex.org/W3159611211","https://openalex.org/W4230579564","https://openalex.org/W6635733909","https://openalex.org/W6636501900","https://openalex.org/W6684191040","https://openalex.org/W6737562747","https://openalex.org/W6739651123","https://openalex.org/W6767220907","https://openalex.org/W6814072441"],"related_works":["https://openalex.org/W3014822659","https://openalex.org/W2117826006","https://openalex.org/W4362496757","https://openalex.org/W2051501574","https://openalex.org/W2124627279","https://openalex.org/W2050967184","https://openalex.org/W2566091814","https://openalex.org/W2114937328","https://openalex.org/W2148654711","https://openalex.org/W2608025327"],"abstract_inverted_index":{"Fingerprint":[0],"classification":[1],"is":[2,80,117],"a":[3,73,113,184],"frequent":[4],"approach":[5],"to":[6,48,68,87,107,131,167,189],"deal":[7],"with":[8,187],"very":[9],"large":[10],"scale":[11],"databases":[12],"in":[13,51,173],"fingerprint":[14],"recognition.":[15],"In":[16],"the":[17,32,52,55,70,83,123,129,134,139,148,180,190],"last":[18],"few":[19],"years,":[20],"several":[21],"proposals":[22,149],"based":[23],"on":[24,62,152],"Convolutional":[25],"Neural":[26],"Networks":[27],"have":[28,59],"pushed":[29],"state":[30],"of":[31,54,76,122,142,161,179],"art":[33],"results":[34],"even":[35],"further.":[36],"However,":[37],"it":[38,79],"has":[39],"also":[40],"been":[41],"proven":[42],"that":[43,82],"such":[44],"networks":[45],"are":[46,105,150],"prone":[47],"be":[49],"overconfident":[50],"predictions":[53],"classes,":[56],"which":[57,119,155,172],"may":[58],"an":[60],"impact":[61,141],"their":[63],"performance.":[64],"This":[65],"paper":[66],"aims":[67],"study":[69],"problem":[71],"from":[72,92],"systematic":[74],"point":[75],"view.":[77],"First,":[78],"determined":[81],"most":[84],"common":[85],"network":[86],"classify":[88],"fingerprints":[89,160],"does":[90],"suffer":[91],"badly":[93],"calibrated":[94,124],"predictions.":[95],"Second,":[96],"two":[97],"calibration":[98,165],"methods":[99],"(temperature":[100],"scaling":[101],"and":[102,137,158],"Dirichlet":[103,164],"calibration)":[104],"applied":[106],"correct":[108],"for":[109,176],"this":[110],"tendency.":[111],"Third,":[112],"modified":[114],"search":[115],"strategy":[116],"proposed,":[118],"makes":[120],"use":[121],"class":[125,170],"probabilities":[126],"predicted":[127,169],"by":[128],"classifier":[130],"further":[132,177],"reduce":[133],"penetration":[135],"rate":[136],"avoid":[138],"negative":[140],"impostor":[143],"input":[144],"fingerprints.":[145],"Fourth,":[146],"all":[147],"evaluated":[151],"five":[153],"datasets,":[154],"combine":[156],"synthetic":[157],"real":[159],"different":[162],"qualities.":[163],"led":[166],"improved":[168],"probabilities,":[171],"turn":[174],"allowed":[175],"reduction":[178],"penetration,":[181],"while":[182],"maintaining":[183],"good":[185],"trade-off":[186],"respect":[188],"false":[191],"rejection":[192],"rate.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}