{"id":"https://openalex.org/W2049555395","doi":"https://doi.org/10.1155/2010/158395","title":"A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination","display_name":"A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination","publication_year":2010,"publication_date":"2010-06-14","ids":{"openalex":"https://openalex.org/W2049555395","doi":"https://doi.org/10.1155/2010/158395","mag":"2049555395"},"language":"en","primary_location":{"id":"doi:10.1155/2010/158395","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/158395","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/158395","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/158395","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108102195","display_name":"Sung Won Park","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sung Won Park","raw_affiliation_strings":["Electrical and Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA","Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057959136","display_name":"Marios Savvides","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marios Savvides","raw_affiliation_strings":["Electrical and Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA","Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108102195"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":2.5854,"has_fulltext":true,"cited_by_count":36,"citation_normalized_percentile":{"value":0.90369868,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2010","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9825999736785889,"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/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.8987587094306946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7481037974357605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7256112098693848},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6563720107078552},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6523106098175049},{"id":"https://openalex.org/keywords/multilinear-map","display_name":"Multilinear map","score":0.6211545467376709},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5811454653739929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5800999999046326},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5636493563652039},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5259407162666321},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.5115259885787964},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5104276537895203},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.49718597531318665},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35184070467948914},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34365832805633545}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.8987587094306946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7481037974357605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7256112098693848},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6563720107078552},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6523106098175049},{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.6211545467376709},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5811454653739929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5800999999046326},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5636493563652039},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5259407162666321},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.5115259885787964},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5104276537895203},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.49718597531318665},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35184070467948914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34365832805633545},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2010/158395","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/158395","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/158395","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d36b057a884d4950be0a4a2657834e02","is_oa":true,"landing_page_url":"https://doaj.org/article/d36b057a884d4950be0a4a2657834e02","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2010 (2010)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2010/158395","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/158395","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/158395","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2049555395.pdf","grobid_xml":"https://content.openalex.org/works/W2049555395.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1480487340","https://openalex.org/W1770825568","https://openalex.org/W1985809919","https://openalex.org/W2006793117","https://openalex.org/W2013912476","https://openalex.org/W2041657594","https://openalex.org/W2072509929","https://openalex.org/W2096692604","https://openalex.org/W2100779717","https://openalex.org/W2124925761","https://openalex.org/W2125874614","https://openalex.org/W2131691213","https://openalex.org/W2134262590","https://openalex.org/W2135346934","https://openalex.org/W2138451337","https://openalex.org/W2140095548","https://openalex.org/W2150412725","https://openalex.org/W2150796457","https://openalex.org/W2526468814","https://openalex.org/W2596954818","https://openalex.org/W2798909945","https://openalex.org/W4229971152","https://openalex.org/W4245550249","https://openalex.org/W4301409532"],"related_works":["https://openalex.org/W2141981133","https://openalex.org/W1963649114","https://openalex.org/W2068218029","https://openalex.org/W1978302214","https://openalex.org/W2025089370","https://openalex.org/W2162393942","https://openalex.org/W1984472287","https://openalex.org/W3147024994","https://openalex.org/W1741177776","https://openalex.org/W2371177901"],"abstract_inverted_index":{"Linear":[0],"Discriminant":[1,87,115],"Analysis":[2,8,116],"(LDA)":[3],"and":[4,24,53,58,68,123,133,158],"Multilinear":[5],"Principal":[6],"Component":[7],"(MPCA)":[9],"are":[10,39,134],"leading":[11],"subspace":[12],"methods":[13,43],"for":[14,160],"achieving":[15],"dimension":[16],"reduction":[17],"based":[18],"on":[19],"supervised":[20],"learning.":[21],"Both":[22],"LDA":[23,52,78,157],"MPCA":[25,54,159],"use":[26],"class":[27],"labels":[28],"of":[29,77,111,131,155],"data":[30],"samples":[31,38],"to":[32,48,79,90,136],"calculate":[33],"subspaces":[34],"onto":[35],"which":[36,104],"these":[37],"projected.":[40],"Furthermore,":[41],"both":[42,156],"have":[44,64],"been":[45,65],"successfully":[46],"applied":[47,66],"face":[49,161],"recognition.":[50,162],"Although":[51],"share":[55],"common":[56],"goals":[57],"methodologies,":[59],"in":[60],"previous":[61],"research":[62],"they":[63],"separately":[67],"independently.":[69],"In":[70,145],"this":[71,146],"paper,":[72],"we":[73],"propose":[74],"an":[75],"extension":[76],"multiple":[80],"factor":[81],"frameworks.":[82],"Our":[83],"proposed":[84,149],"method,":[85],"Multifactor":[86,114],"Analysis,":[88],"aims":[89],"obtain":[91],"multilinear":[92],"projections":[93],"that":[94,127],"maximize":[95],"the":[96,101,106,129,152],"between-class":[97],"scatter":[98],"while":[99],"minimizing":[100],"withinclass":[102],"scatter,":[103],"is":[105],"same":[107],"core":[108],"fundamental":[109],"objective":[110],"LDA.":[112],"Moreover,":[113],"(MDA),":[117],"like":[118],"MPCA,":[119],"uses":[120],"multifactor":[121],"analysis":[122],"calculates":[124],"subject":[125],"parameters":[126],"represent":[128],"characteristics":[130],"subjects":[132],"invariant":[135],"other":[137],"changes,":[138],"such":[139],"as":[140],"viewpoints":[141],"or":[142],"lighting":[143],"conditions.":[144],"way,":[147],"our":[148],"MDA":[150],"combines":[151],"best":[153],"virtues":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
