{"id":"https://openalex.org/W1993620392","doi":"https://doi.org/10.1109/nabic.2009.5393861","title":"EBFS-Fisher: An efficient algorithm for LDA-based face recognition","display_name":"EBFS-Fisher: An efficient algorithm for LDA-based face recognition","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W1993620392","doi":"https://doi.org/10.1109/nabic.2009.5393861","mag":"1993620392"},"language":"en","primary_location":{"id":"doi:10.1109/nabic.2009.5393861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2009.5393861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 World Congress on Nature &amp; Biologically Inspired Computing (NaBIC)","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/A5072703440","display_name":"Rutuparna Panda","orcid":"https://orcid.org/0000-0002-8676-0144"},"institutions":[{"id":"https://openalex.org/I185065464","display_name":"Veer Surendra Sai University of Technology","ror":"https://ror.org/02yghbg68","country_code":"IN","type":"education","lineage":["https://openalex.org/I185065464"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rutuparna Panda","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Orissa, India","Department of Electronics and Telecommunication Engg., Veer Surendra Sai University of Technology, Orissa, Burla-768018, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Orissa, India","institution_ids":["https://openalex.org/I185065464"]},{"raw_affiliation_string":"Department of Electronics and Telecommunication Engg., Veer Surendra Sai University of Technology, Orissa, Burla-768018, India","institution_ids":["https://openalex.org/I185065464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064311929","display_name":"Manoj Kumar Naik","orcid":"https://orcid.org/0000-0002-8077-1811"},"institutions":[{"id":"https://openalex.org/I47639616","display_name":"Centurion University of Technology and Management","ror":"https://ror.org/03js1g511","country_code":"IN","type":"education","lineage":["https://openalex.org/I47639616"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manoj Kumar Naik","raw_affiliation_strings":["Department of Electronics and Instrumentation Engineering, Jagannath Institute for Technology and Management, Paralakhemundi, India","Department of Electronics and Instrumentation Engg., Jagannath Institute for Technology & Management, Paralakhemundi - 761 211, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Instrumentation Engineering, Jagannath Institute for Technology and Management, Paralakhemundi, India","institution_ids":["https://openalex.org/I47639616"]},{"raw_affiliation_string":"Department of Electronics and Instrumentation Engg., Jagannath Institute for Technology & Management, Paralakhemundi - 761 211, India","institution_ids":["https://openalex.org/I47639616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2682,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.41608743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"83","issue":null,"first_page":"1041","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987999796867371,"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":0.9987999796867371,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9266999959945679,"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/computer-science","display_name":"Computer science","score":0.5485060214996338},{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.526191771030426},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5175182819366455},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.4952005445957184},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.48784756660461426},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.48001235723495483},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.45730406045913696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42206934094429016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40154021978378296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3946579098701477},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.3744531273841858},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3319004774093628},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3281008303165436},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.12142738699913025}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5485060214996338},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.526191771030426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5175182819366455},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.4952005445957184},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.48784756660461426},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.48001235723495483},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.45730406045913696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42206934094429016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40154021978378296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3946579098701477},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.3744531273841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3319004774093628},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3281008303165436},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.12142738699913025},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nabic.2009.5393861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2009.5393861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 World Congress on Nature &amp; Biologically Inspired Computing (NaBIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1985809919","https://openalex.org/W1991390949","https://openalex.org/W2033419168","https://openalex.org/W2115689562","https://openalex.org/W2116019577","https://openalex.org/W2118168434","https://openalex.org/W2121647436","https://openalex.org/W2122122715","https://openalex.org/W2134231130","https://openalex.org/W2134262590","https://openalex.org/W2135463994","https://openalex.org/W2136405083","https://openalex.org/W2138451337","https://openalex.org/W2143340430"],"related_works":["https://openalex.org/W2014162767","https://openalex.org/W2809283963","https://openalex.org/W2367260997","https://openalex.org/W2115729582","https://openalex.org/W2363406585","https://openalex.org/W2106492215","https://openalex.org/W2326694407","https://openalex.org/W2082859007","https://openalex.org/W2378719652","https://openalex.org/W2164831575"],"abstract_inverted_index":{"This":[0,86],"paper":[1,107],"presents":[2],"a":[3,40,57,77,83,109,116,132],"new":[4,110],"algorithm":[5,24,111,141,149],"for":[6,76,95,180],"LDA-based":[7],"face":[8,99],"recognition":[9,100],"with":[10],"selection":[11],"of":[12,80,98,134,154,170],"optimal":[13,30,49,93],"principal":[14],"components":[15],"using":[16,56],"E-coli":[17],"Bacterial":[18],"Foraging":[19],"Strategy":[20],"(EBFS).":[21],"A":[22],"GA-PCA":[23],"has":[25,43],"been":[26,44],"reported":[27],"to":[28,46,51,165,198],"find":[29,47],"eigenvalues":[31],"and":[32,71],"corresponding":[33],"eigenvectors":[34,50,94],"in":[35,54,67,69,82,91,161,167],"LDA.":[36],"In":[37],"their":[38],"paper,":[39],"fitness":[41],"function":[42,119,125],"proposed":[45,139,148],"the":[48,62,103,123,147,152,157,168,185],"be":[52,194],"used":[53,65,179],"LDA":[55],"Genetic":[58],"Algorithm":[59],"(GA).":[60],"However,":[61],"crossover":[63],"method":[64],"results":[66,183],"differences":[68],"offspring":[70],"mutation":[72],"never":[73],"allow":[74],"us":[75,90,164],"physical":[78],"dispersal":[79],"child":[81],"chosen":[84],"area.":[85],"may":[87],"not":[88],"help":[89,163],"finding":[92],"improvising":[96],"accuracy":[97],"algorithm.":[101],"On":[102],"other":[104],"hand,":[105],"this":[106],"proposes":[108],"called":[112],"EBFS-Fisher":[113,140],"which":[114],"uses":[115],"nutrient":[117],"concentration":[118],"(cost":[120],"function).":[121],"Here":[122],"cost":[124],"is":[126,178],"maximized":[127],"through":[128],"hill":[129],"climbing":[130],"via":[131],"type":[133],"biased":[135],"random":[136,158],"walk.":[137],"The":[138,174],"offers":[142],"two":[143],"additional":[144],"advantages.":[145],"First,":[146],"can":[150,193],"supplement":[151],"features":[153],"GA.":[155],"Second,":[156],"bias":[159],"incorporated":[160],"EBFS":[162],"move":[166],"direction":[169],"increasingly":[171],"favorable":[172],"environment.":[173],"Yale":[175],"data":[176],"base":[177],"evaluation.":[181],"Experimental":[182],"depict":[184],"fact":[186],"that":[187],"about":[188],"3%":[189],"(Rank":[190],"1)":[191],"improvement":[192],"achieved":[195],"as":[196],"compared":[197],"GA-Fisher.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
