{"id":"https://openalex.org/W2158030661","doi":"https://doi.org/10.1142/s0218126616500171","title":"Test Sample Oriented Dictionary Learning for Face Recognition","display_name":"Test Sample Oriented Dictionary Learning for Face Recognition","publication_year":2015,"publication_date":"2015-10-15","ids":{"openalex":"https://openalex.org/W2158030661","doi":"https://doi.org/10.1142/s0218126616500171","mag":"2158030661"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126616500171","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126616500171","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-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/A5082787229","display_name":"Zhengming Li","orcid":"https://orcid.org/0000-0001-7330-8864"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengming Li","raw_affiliation_strings":["Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China","Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China","institution_ids":["https://openalex.org/I4210122543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082787229"],"corresponding_institution_ids":["https://openalex.org/I204983213","https://openalex.org/I4210122543"],"apc_list":null,"apc_paid":null,"fwci":0.3673,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6860348,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"25","issue":"04","first_page":"1650017","last_page":"1650017"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991000294685364,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.9865999817848206,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6660556793212891},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.617606520652771},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.601078450679779},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5503590703010559},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.536463737487793},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.5325462222099304},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5268315672874451},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5170758366584778},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48904356360435486},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.4758736193180084},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.45487791299819946},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.42682501673698425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4254315495491028}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6660556793212891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.617606520652771},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.601078450679779},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5503590703010559},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.536463737487793},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.5325462222099304},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5268315672874451},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5170758366584778},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48904356360435486},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.4758736193180084},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.45487791299819946},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.42682501673698425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4254315495491028},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126616500171","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126616500171","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8500000238418579}],"awards":[],"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":31,"referenced_works":["https://openalex.org/W16591383","https://openalex.org/W1591385104","https://openalex.org/W1782590233","https://openalex.org/W1890834058","https://openalex.org/W1963932623","https://openalex.org/W1986560547","https://openalex.org/W1992405901","https://openalex.org/W1993049083","https://openalex.org/W2027805700","https://openalex.org/W2033419168","https://openalex.org/W2073126393","https://openalex.org/W2097018403","https://openalex.org/W2097323375","https://openalex.org/W2097486709","https://openalex.org/W2108024382","https://openalex.org/W2114122776","https://openalex.org/W2118990207","https://openalex.org/W2122922389","https://openalex.org/W2127271355","https://openalex.org/W2129812935","https://openalex.org/W2130951397","https://openalex.org/W2132467081","https://openalex.org/W2134563198","https://openalex.org/W2135942013","https://openalex.org/W2140245639","https://openalex.org/W2149861038","https://openalex.org/W2156593994","https://openalex.org/W2157785665","https://openalex.org/W2160547390","https://openalex.org/W2162583786","https://openalex.org/W2296399167"],"related_works":["https://openalex.org/W2509955295","https://openalex.org/W2016265625","https://openalex.org/W2111634407","https://openalex.org/W2552089492","https://openalex.org/W4205656132","https://openalex.org/W2008821896","https://openalex.org/W2157785665","https://openalex.org/W2048023787","https://openalex.org/W3082420294","https://openalex.org/W2068941797"],"abstract_inverted_index":{"Dictionary":[0],"learning":[1,33,75,142],"(DL)":[2],"algorithms":[3,15,187],"have":[4],"shown":[5],"very":[6],"good":[7],"performance":[8],"in":[9,31,56],"face":[10,79,197],"recognition.":[11,80],"However,":[12],"conventional":[13,62],"DL":[14,39,49,63,107,183],"exploit":[16],"only":[17,133],"the":[18,23,28,32,43,58,83,87,103,122,128,141,151,158,169,181],"training":[19],"samples":[20,60,175],"to":[21,53,108,139],"obtain":[22,121],"dictionary":[24,74,112,123,149],"and":[25,94,102,124,184],"totally":[26],"neglect":[27],"test":[29,47,59,71,100,115,129,153,162,174],"sample":[30,72,101,130,154],"procedure.":[34],"As":[35],"a":[36,70,99,110,148,190],"result,":[37],"if":[38],"is":[40],"associated":[41],"with":[42],"linear":[44,84,191],"representation":[45,85,125],"of":[46,98,106,127,160,180],"sample,":[48],"may":[50],"be":[51],"able":[52],"perform":[54],"better":[55],"classifying":[57],"than":[61,178],"algorithms.":[64],"In":[65,137],"this":[66],"paper,":[67],"we":[68,146],"propose":[69],"oriented":[73],"(TSODL)":[76],"algorithm":[77,171],"for":[78,113,150],"We":[81],"combine":[82],"(including":[86],"[Formula:":[88,91,95],"see":[89,92,96],"text]-norm,":[90],"text]-norm":[93],"text]-norm)":[97],"basic":[104],"model":[105],"learn":[109],"single":[111],"each":[114],"sample.":[116],"Thus,":[117],"it":[118],"can":[119,172],"simultaneously":[120],"coefficients":[126],"by":[131,155,188],"minimizing":[132],"one":[134],"objective":[135],"function.":[136],"order":[138],"make":[140],"procedure":[143],"more":[144,176],"efficient,":[145],"initialize":[147],"new":[152],"selecting":[156],"from":[157],"dictionaries":[159],"previous":[161],"samples.":[163],"The":[164],"experimental":[165],"results":[166],"show":[167],"that":[168],"TSODL":[170],"classify":[173],"accurately":[177],"some":[179],"state-of-the-art":[182],"sparse":[185],"coding":[186],"using":[189],"classifier":[192],"method":[193],"on":[194],"three":[195],"public":[196],"databases.":[198]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}