{"id":"https://openalex.org/W3015083589","doi":"https://doi.org/10.32604/cmc.2020.09044","title":"Left Or Right Hand Classification From Fingerprint Images Using A Deep Neural Network","display_name":"Left Or Right Hand Classification From Fingerprint Images Using A Deep Neural Network","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3015083589","doi":"https://doi.org/10.32604/cmc.2020.09044","mag":"3015083589"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2020.09044","doi":"https://doi.org/10.32604/cmc.2020.09044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2020.09044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2020.09044","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011517497","display_name":"Junseob Kim","orcid":"https://orcid.org/0000-0002-6959-5271","observed_orcids":["https://orcid.org/0000-0002-6959-5271"]},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junseob Kim","raw_affiliation_strings":["Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea","institution_ids":["https://openalex.org/I24541011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017604964","display_name":"Beanbonyka Rim","orcid":"https://orcid.org/0000-0003-1232-0610","observed_orcids":["https://orcid.org/0000-0003-1232-0610"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rim Beanbonyka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076658552","display_name":"Nak-Jun Sung","orcid":"https://orcid.org/0000-0003-3514-7152","observed_orcids":["https://orcid.org/0000-0003-3514-7152"]},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nak-Jun Sung","raw_affiliation_strings":["Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea","institution_ids":["https://openalex.org/I24541011"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006130083","display_name":"Min Hong","orcid":"https://orcid.org/0000-0001-9963-5521","observed_orcids":["https://orcid.org/0000-0001-9963-5521"]},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min Hong","raw_affiliation_strings":["Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Soonchunhyang University, 31538, Asan, Korea. 2 Department of Computer Software Engineering, Soonchunhyang University, 31538, Asan, Korea","institution_ids":["https://openalex.org/I24541011"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0793,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.74519407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"62","issue":"3","first_page":"17","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9994999766349792,"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":0.9994999766349792,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.00019999999494757503,"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/T10036","display_name":"Advanced Neural Network Applications","score":9.999999747378752e-05,"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/fingerprint-images","display_name":"fingerprint images","score":0.6740000247955322},{"id":"https://openalex.org/keywords/fingerprint-biometrics","display_name":"fingerprint biometrics","score":0.6940000057220459},{"id":"https://openalex.org/keywords/automated-fingerprint-identification-systems","display_name":"automated fingerprint identification systems","score":0.8090000152587891},{"id":"https://openalex.org/keywords/resnet-50","display_name":"ResNet50","score":0.8510000109672546},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8769999742507935},{"id":"https://openalex.org/keywords/yolo","display_name":"YOLO","score":0.722000002861023},{"id":"https://openalex.org/keywords/vgg16","display_name":"VGG16","score":0.9120000004768372},{"id":"https://openalex.org/keywords/alexnet","display_name":"AlexNet","score":0.8849999904632568}],"concepts":[{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.8407233357429504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8080854415893555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7683990001678467},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7161840796470642},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6738232970237732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6475625038146973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6187997460365295},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.4938083589076996},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47863808274269104},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.4635011553764343},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43919622898101807},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.43016955256462097},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2020.09044","doi":"https://doi.org/10.32604/cmc.2020.09044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2020.09044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2020.09044","doi":"https://doi.org/10.32604/cmc.2020.09044","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2020.09044","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2044097773","https://openalex.org/W2304245051","https://openalex.org/W2584050167","https://openalex.org/W2756537198","https://openalex.org/W2786785058","https://openalex.org/W2791928595","https://openalex.org/W2931853024","https://openalex.org/W2946861034"],"related_works":["https://openalex.org/W3043256287","https://openalex.org/W4382119370","https://openalex.org/W4360989104","https://openalex.org/W4403503126","https://openalex.org/W3159611211","https://openalex.org/W4407303606","https://openalex.org/W4407129199","https://openalex.org/W2811136374","https://openalex.org/W4398756500","https://openalex.org/W4223505845"],"abstract_inverted_index":{"Fingerprint":[0,42],"security":[1],"technology":[2],"has":[3],"attracted":[4],"a":[5,29,37,46,54,65,86,166],"great":[6],"deal":[7],"of":[8,14,67,161,168,196],"attention":[9],"in":[10,142],"recent":[11],"years":[12],"because":[13],"its":[15],"unique":[16],"biometric":[17],"information":[18],"that":[19,137],"does":[20],"not":[21],"change":[22],"over":[23],"an":[24,182,194],"individual\u2019s":[25],"lifetime":[26],"and":[27,32,122,154],"is":[28,45],"highly":[30],"reliable":[31],"secure":[33],"way":[34],"to":[35,94,129,157],"identify":[36],"certain":[38],"individuals.":[39],"AFIS":[40,60],"(Automated":[41],"Identification":[43],"System)":[44],"system":[47,89],"used":[48,141,147],"by":[49,57,78],"Korean":[50],"police":[51],"for":[52,152],"identifying":[53],"specific":[55],"person":[56],"fingerprint.":[58],"The":[59],"system,":[61],"however,":[62],"only":[63],"selects":[64],"list":[66],"possible":[68],"candidates":[69],"through":[70],"fingerprints,":[71],"the":[72,101,111,159,162,173,177,188],"exact":[73],"individual":[74],"must":[75],"be":[76],"found":[77,172],"fingerprint":[79,150,156,184],"experts.":[80],"In":[81,106],"this":[82,107,130],"paper,":[83,108],"we":[84,109,171],"designed":[85],"deep":[87,91,134],"learning":[88,135],"using":[90],"convolution":[92],"network":[93,175],"categorize":[95],"fingerprints":[96],"as":[97],"coming":[98],"from":[99,187],"either":[100],"left":[102,189],"or":[103,190],"right":[104,191],"hand.":[105],"applied":[110],"Classic":[112],"CNN":[113],"(Convolutional":[114],"Neural":[115],"Network),":[116,120],"AlexNet,":[117],"Resnet50":[118],"(Residual":[119],"VGG-16,":[121],"YOLO":[123],"(You":[124],"Only":[125],"Look":[126],"Once)":[127],"networks":[128],"problem,":[131],"these":[132],"are":[133],"architectures":[136],"have":[138],"been":[139],"widely":[140],"image":[143,185],"analysis":[144],"research.":[145],"We":[146],"total":[148],"9,080":[149],"images":[151],"training":[153],"1,000":[155],"test":[158],"performance":[160],"proposed":[163],"model.":[164],"As":[165],"result":[167],"our":[169],"tests,":[170],"ResNet50":[174],"performed":[176],"best":[178],"at":[179],"determining":[180],"if":[181],"input":[183],"came":[186],"hand":[192],"with":[193],"accuracy":[195],"96.80%.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-10-02T10:15:12.560889","created_date":"2020-04-10T00:00:00"}