{"id":"https://openalex.org/W2751500086","doi":"https://doi.org/10.1109/icmew.2017.8026289","title":"Domain transfer sparse representation for single sample face recognition","display_name":"Domain transfer sparse representation for single sample face recognition","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2751500086","doi":"https://doi.org/10.1109/icmew.2017.8026289","mag":"2751500086"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5020313082","display_name":"Venice Erin Liong","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Venice Erin Liong","raw_affiliation_strings":["Interdisciplinary Graduate School, Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Interdisciplinary Graduate School, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103689774","display_name":"Haibin Yan","orcid":"https://orcid.org/0009-0008-2979-9895"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibin Yan","raw_affiliation_strings":["School of Automation, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020313082"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.091,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46188827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"54","issue":null,"first_page":"663","last_page":"668"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980000257492065,"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":0.9980000257492065,"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":0.9876000285148621,"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/T11448","display_name":"Face recognition and analysis","score":0.9850000143051147,"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/discriminative-model","display_name":"Discriminative model","score":0.8759545087814331},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7377309203147888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.677901029586792},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6329260468482971},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6047240495681763},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5836384296417236},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5823604464530945},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5697956085205078},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5689767599105835},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5444716811180115},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5354155898094177},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5342826843261719},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.532968282699585},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49743059277534485},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37812358140945435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18279263377189636}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8759545087814331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7377309203147888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.677901029586792},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6329260468482971},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6047240495681763},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5836384296417236},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5823604464530945},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5697956085205078},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5689767599105835},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5444716811180115},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5354155898094177},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5342826843261719},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.532968282699585},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49743059277534485},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37812358140945435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18279263377189636},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2017.8026289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1984608801","https://openalex.org/W2033419168","https://openalex.org/W2092131162","https://openalex.org/W2098012923","https://openalex.org/W2100240926","https://openalex.org/W2107298017","https://openalex.org/W2112274848","https://openalex.org/W2113606819","https://openalex.org/W2120100419","https://openalex.org/W2129812935","https://openalex.org/W2144583419","https://openalex.org/W2160039895","https://openalex.org/W2963689635","https://openalex.org/W2994340921","https://openalex.org/W6635552349","https://openalex.org/W6676759488","https://openalex.org/W6676903177","https://openalex.org/W6683390231"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2153315159","https://openalex.org/W2000332559","https://openalex.org/W1959834984","https://openalex.org/W1580457994","https://openalex.org/W1973963881","https://openalex.org/W2769045829","https://openalex.org/W2379945344","https://openalex.org/W2112414222","https://openalex.org/W2912726270"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,48,87,112],"propose":[4,49],"a":[5,50,95],"new":[6],"single":[7],"sample":[8],"face":[9,164],"recognition":[10],"approach":[11],"under":[12],"the":[13,71,76,89,102,106,114,122,143,148,157,168,171],"widely":[14],"used":[15],"sparse":[16,53],"representation-based":[17,54],"classification":[18,55],"(SRC)":[19],"framework.":[20],"Previous":[21],"work":[22],"has":[23],"shown":[24],"that":[25,125],"SRC":[26],"only":[27],"works":[28],"well":[29],"when":[30],"there":[31],"are":[32,81],"sufficient":[33],"number":[34],"of":[35,117,142,170],"training":[36,73,79,107],"samples":[37,146],"per":[38],"person":[39],"and":[40,67,78],"not":[41],"suitable":[42],"for":[43],"SSFR.":[44],"To":[45,109],"address":[46],"this,":[47,111],"domain":[51],"transfer":[52,98],"(DT-SRC)":[56],"method":[57],"by":[58,139],"using":[59],"an":[60],"auxiliary":[61,77,103,149],"dataset":[62],"to":[63,93,97,105,135,151],"learn":[64,94],"intra-class":[65,99],"variations":[66,100],"transferring":[68],"them":[69],"into":[70],"single-sample":[72],"set.":[74,108],"Since":[75],"sets":[80],"likely":[82],"captured":[83],"in":[84,121,147,156],"different":[85],"environments,":[86],"apply":[88],"dictionary":[90],"learning":[91],"technique":[92],"meta-space":[96,123],"from":[101],"set":[104,150],"achieve":[110],"minimize":[113],"distribution":[115],"difference":[116],"these":[118],"two":[119],"datasets":[120,166],"so":[124],"such":[126],"information":[127,145,155],"can":[128],"be":[129],"effectively":[130],"transferred.":[131],"We":[132],"extend":[133],"DT-SRC":[134,137],"discriminative":[136,154],"(DDTSRC)":[138],"making":[140],"use":[141],"label":[144],"exploit":[152],"more":[153],"learned":[158],"meta-space.":[159],"Experimental":[160],"results":[161],"on":[162],"three":[163],"benchmark":[165],"demonstrate":[167],"effectiveness":[169],"proposed":[172],"approach.":[173]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}