{"id":"https://openalex.org/W3119545081","doi":"https://doi.org/10.1109/ijcb48548.2020.9304871","title":"LDM-DAGSVM: Learning Distance Metric via DAG Support Vector Machine for Ear Recognition Problem","display_name":"LDM-DAGSVM: Learning Distance Metric via DAG Support Vector Machine for Ear Recognition Problem","publication_year":2020,"publication_date":"2020-09-28","ids":{"openalex":"https://openalex.org/W3119545081","doi":"https://doi.org/10.1109/ijcb48548.2020.9304871","mag":"3119545081"},"language":"en","primary_location":{"id":"doi:10.1109/ijcb48548.2020.9304871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb48548.2020.9304871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5040546817","display_name":"Ibrahim Omara","orcid":"https://orcid.org/0000-0003-3243-990X"},"institutions":[{"id":"https://openalex.org/I63601056","display_name":"Menoufia University","ror":"https://ror.org/05sjrb944","country_code":"EG","type":"education","lineage":["https://openalex.org/I63601056"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Ibrahim Omara","raw_affiliation_strings":["Department of Mathematics and computer Science, Faculty of Science, Menoufia University"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and computer Science, Faculty of Science, Menoufia University","institution_ids":["https://openalex.org/I63601056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056094234","display_name":"Guangzhi Ma","orcid":"https://orcid.org/0000-0003-1401-908X"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangzhi Ma","raw_affiliation_strings":["School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084444817","display_name":"Enmin Song","orcid":"https://orcid.org/0000-0003-1434-5758"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enmin Song","raw_affiliation_strings":["School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040546817"],"corresponding_institution_ids":["https://openalex.org/I63601056"],"apc_list":null,"apc_paid":null,"fwci":1.0652,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.78118689,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9998999834060669,"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":0.9998999834060669,"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/T11448","display_name":"Face recognition and analysis","score":0.98580002784729,"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/T11800","display_name":"User Authentication and Security Systems","score":0.9593999981880188,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/support-vector-machine","display_name":"Support vector machine","score":0.8157281875610352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7280462384223938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6709228754043579},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.6190696954727173},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6169356107711792},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5911514759063721},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5766525268554688},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.43842217326164246},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4313563406467438},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16775840520858765},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07263696193695068}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8157281875610352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7280462384223938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6709228754043579},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.6190696954727173},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6169356107711792},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5911514759063721},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5766525268554688},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43842217326164246},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4313563406467438},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16775840520858765},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07263696193695068},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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":1,"locations":[{"id":"doi:10.1109/ijcb48548.2020.9304871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb48548.2020.9304871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G4981920774","display_name":null,"funder_award_id":"81671768","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6538982792","display_name":null,"funder_award_id":"2017YFC0112804","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W118329446","https://openalex.org/W1595104675","https://openalex.org/W1865963168","https://openalex.org/W1873533840","https://openalex.org/W1978452970","https://openalex.org/W1996118086","https://openalex.org/W2059334914","https://openalex.org/W2062112832","https://openalex.org/W2076175299","https://openalex.org/W2096350233","https://openalex.org/W2097235937","https://openalex.org/W2098368939","https://openalex.org/W2112846322","https://openalex.org/W2113366599","https://openalex.org/W2138963285","https://openalex.org/W2153635508","https://openalex.org/W2157239837","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2167042540","https://openalex.org/W2310200922","https://openalex.org/W2321462649","https://openalex.org/W2402404238","https://openalex.org/W2405680777","https://openalex.org/W2514810124","https://openalex.org/W2548197316","https://openalex.org/W2551082533","https://openalex.org/W2620429297","https://openalex.org/W2679426462","https://openalex.org/W2726994734","https://openalex.org/W2738416945","https://openalex.org/W2739291585","https://openalex.org/W2770777404","https://openalex.org/W2770897789","https://openalex.org/W2777157629","https://openalex.org/W2782072221","https://openalex.org/W2791769710","https://openalex.org/W2792027052","https://openalex.org/W2794657927","https://openalex.org/W2795729594","https://openalex.org/W2798101467","https://openalex.org/W2800628486","https://openalex.org/W2888747523","https://openalex.org/W2889353819","https://openalex.org/W2895160458","https://openalex.org/W2900389133","https://openalex.org/W2964247301","https://openalex.org/W2976365704","https://openalex.org/W3105744362","https://openalex.org/W6674219269","https://openalex.org/W6677337051","https://openalex.org/W6683380652","https://openalex.org/W6729520762","https://openalex.org/W6741603768","https://openalex.org/W6756569333"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W2355463328","https://openalex.org/W2402648945","https://openalex.org/W939486154","https://openalex.org/W1431147547","https://openalex.org/W2771741613","https://openalex.org/W2807757380","https://openalex.org/W2075445622","https://openalex.org/W2153677915","https://openalex.org/W128856181"],"abstract_inverted_index":{"Recently,":[0],"the":[1,56,66,87,90,139,153,158,171,179],"ear":[2,57,74,130,136,168,181,202],"recognition":[3,58,75,131,182],"system":[4],"takes":[5],"more":[6],"increasingly":[7],"interesting":[8],"for":[9,55,73,113,129,197],"many":[10],"applications,":[11],"especially,":[12],"in":[13,103],"immigration":[14],"system,":[15],"forensic,":[16],"and":[17,23,39,89,122,156,170,195,200],"surveillance":[18],"applications.":[19],"For":[20],"face":[21],"re-identification":[22],"image":[24],"classification,":[25],"metric":[26,46,82,111,121,148],"learning":[27,32,47,80,112,119],"has":[28,50],"significantly":[29],"improved":[30],"machine":[31],"accuracies":[33],"by":[34,115],"using":[35],"K-Nearest":[36],"Neighbor":[37],"(KNN)":[38],"Support":[40],"Vector":[41],"Machine":[42],"(SVM)":[43],"classifiers.":[44],"However,":[45],"via":[48,84,149],"SVM":[49,85,94,114,126,150],"not":[51],"yet":[52],"been":[53],"investigated":[54],"problem.":[59],"To":[60],"achieve":[61,174],"better":[62,175],"generalization":[63],"ability":[64],"than":[65],"traditional":[67],"previous":[68],"classifiers,":[69],"a":[70,117,145],"novel":[71],"framework":[72],"is":[76],"proposed":[77,140,185],"based":[78],"on":[79,166],"distance":[81,120,147],"(LDM)":[83],"since":[86],"LDM":[88],"directed":[91,123],"acyclic":[92,124],"graph":[93,125],"(DAGSVM)":[95],"are":[96,164],"two":[97],"emerging":[98],"techniques":[99],"which":[100],"perform":[101],"outstanding":[102],"dealing":[104],"with":[105,178],"classification":[106,189],"problems.":[107],"This":[108],"work":[109],"considers":[110],"proposing":[116],"hybrid":[118],"(LDM-DAGSVM)":[127],"model":[128],"system.":[132],"Different":[133],"from":[134],"existing":[135],"biometric":[137],"methods,":[138],"approach":[141,186],"aims":[142],"to":[143,151,192],"learn":[144],"Mahalanobis":[146],"maximize":[152],"inter-class":[154],"variations":[155],"minimize":[157],"intra-class":[159],"variations,":[160],"simultaneously.":[161],"The":[162,184],"experiments":[163],"conducted":[165],"complicated":[167],"datasets":[169],"results":[172],"can":[173,187],"performance":[176],"compared":[177],"state-of-the-art":[180],"methods.":[183],"get":[188],"accuracy":[190],"up":[191],"98.79%,":[193],"98.70%,":[194],"84.30%":[196],"AWE,":[198],"AME":[199],"WPUT":[201],"datasets,":[203],"respectively.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}