{"id":"https://openalex.org/W4200352759","doi":"https://doi.org/10.1109/wcsp52459.2021.9613587","title":"L1-Norm Distance Metric Learning for Gait Recognition","display_name":"L1-Norm Distance Metric Learning for Gait Recognition","publication_year":2021,"publication_date":"2021-10-20","ids":{"openalex":"https://openalex.org/W4200352759","doi":"https://doi.org/10.1109/wcsp52459.2021.9613587"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp52459.2021.9613587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp52459.2021.9613587","pdf_url":null,"source":{"id":"https://openalex.org/S4363607893","display_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","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 13th International Conference on Wireless Communications and Signal Processing (WCSP)","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/A5065383812","display_name":"Dong Liu","orcid":"https://orcid.org/0000-0002-1486-3793"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Liu","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043477487","display_name":"Junlin Hu","orcid":"https://orcid.org/0000-0002-0117-3494"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junlin Hu","raw_affiliation_strings":["School of Software, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9589,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.74091983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.586033821105957},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5593671202659607},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5423830151557922},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4609862267971039},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0959177017211914}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.586033821105957},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5593671202659607},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5423830151557922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4609862267971039},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0959177017211914},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcsp52459.2021.9613587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp52459.2021.9613587","pdf_url":null,"source":{"id":"https://openalex.org/S4363607893","display_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","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 13th International Conference on Wireless Communications and Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G4974848217","display_name":null,"funder_award_id":"62006013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2036509154","https://openalex.org/W2068042582","https://openalex.org/W2076434944","https://openalex.org/W2096733369","https://openalex.org/W2104335344","https://openalex.org/W2126680226","https://openalex.org/W2138621090","https://openalex.org/W2169495281","https://openalex.org/W2510190030","https://openalex.org/W2517225990","https://openalex.org/W2555897561","https://openalex.org/W2605102252","https://openalex.org/W2753064086","https://openalex.org/W2767325013","https://openalex.org/W2900572353","https://openalex.org/W2960957522","https://openalex.org/W2963301258","https://openalex.org/W2981574046","https://openalex.org/W2987157497","https://openalex.org/W3045219832","https://openalex.org/W3094345665","https://openalex.org/W3118694472","https://openalex.org/W3123329750","https://openalex.org/W3164707500","https://openalex.org/W4294338724","https://openalex.org/W6675575696","https://openalex.org/W6719959352","https://openalex.org/W6730323794"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W1861706286","https://openalex.org/W2390279801","https://openalex.org/W2219338811","https://openalex.org/W2358668433","https://openalex.org/W2167092671","https://openalex.org/W2149583853","https://openalex.org/W2143002539"],"abstract_inverted_index":{"Gait":[0],"recognition":[1],"is":[2,75,91,121],"a":[3,20,56,78,94,129],"biometric":[4],"technology":[5],"which":[6,97,120],"realizes":[7],"human":[8,14],"identification":[9],"through":[10],"the":[11,46,72,88,112,135,140,144,147],"characteristics":[12],"of":[13,23,48,83,102,131,146],"movement":[15],"during":[16],"walking.":[17],"It":[18],"has":[19],"wide":[21],"range":[22],"potential":[24],"applications":[25],"in":[26,71,87],"security,":[27],"surveillance,":[28],"medical":[29],"and":[30,139],"other":[31],"fields.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"propose":[37],"a$L$1-norm":[38],"distance":[39,66,113],"Metric":[40],"Learning":[41],"($L$1ML)":[42],"method":[43],"to":[44,54,110,124],"study":[45],"problem":[47],"gait":[49,137],"recognition.":[50],"The":[51],"proposed$L$1ML":[52],"aims":[53],"learn":[55],"linear":[57],"transformation":[58],"under":[59],"large":[60,95],"margin":[61],"framework":[62],"so":[63],"that":[64,82,108],"the$L$1-norm":[65],"between":[67,114],"positive":[68],"sample":[69,85],"pairs":[70,86],"transformed":[73,89],"subspace":[74,90],"smaller":[76],"than":[77,93],"small":[79],"threshold,":[80,96],"while":[81],"negative":[84],"greater":[92],"can":[98],"effectively":[99],"distinguish":[100],"samples":[101],"different":[103],"subjects.":[104],"Unlike":[105],"traditional":[106],"methods":[107],"employ$L$2-norm":[109],"calculate":[111],"samples,":[115],"our":[116],"L1ML":[117],"utilizes$L$1-norm":[118],"distance,":[119],"more":[122],"robust":[123],"outlier":[125],"samples.":[126],"We":[127],"conduct":[128],"series":[130],"comparative":[132],"experiments":[133],"on":[134],"CASIA-B":[136],"dataset,":[138],"experimental":[141],"results":[142],"verify":[143],"effectiveness":[145],"proposed":[148],"method.":[149]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}