{"id":"https://openalex.org/W4293202356","doi":"https://doi.org/10.1504/ijaip.2022.123020","title":"A new fuzzy and Gaussian distribution induced two-directional inverse FDA for feature extraction and face recognition","display_name":"A new fuzzy and Gaussian distribution induced two-directional inverse FDA for feature extraction and face recognition","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4293202356","doi":"https://doi.org/10.1504/ijaip.2022.123020"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijaip.2022.123020","pdf_url":null,"source":{"id":"https://openalex.org/S27191780","display_name":"International Journal of Advanced Intelligence Paradigms","issn_l":"1755-0386","issn":["1755-0386","1755-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5013375317","display_name":"Aniruddha Dey","orcid":null},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aniruddha Dey","raw_affiliation_strings":["Department of Computer Science and Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023323476","display_name":"Shiladitya Chowdhury","orcid":null},"institutions":[],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shiladitya Chowdhury","raw_affiliation_strings":["Department of Master of Computer Application, Techno India, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Master of Computer Application, Techno India, Kolkata, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046175002","display_name":"Jamuna Kanta Sing","orcid":"https://orcid.org/0000-0003-1006-6006"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jamuna Kanta Sing","raw_affiliation_strings":["Department of Computer Science and Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":"22","issue":"1/2","first_page":"148","last_page":"148"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9889,"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":0.9889,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9241,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41861498}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.63436526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5570962},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5526071},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.54598624},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5451798},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.52513456},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4467},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43168783},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.420892},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41861498},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41062728},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijaip.2022.123020","pdf_url":null,"source":{"id":"https://openalex.org/S27191780","display_name":"International Journal of Advanced Intelligence Paradigms","issn_l":"1755-0386","issn":["1755-0386","1755-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.72,"id":"https://metadata.un.org/sdg/10"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W49077413","https://openalex.org/W4244943737","https://openalex.org/W3171199097","https://openalex.org/W2772780115","https://openalex.org/W2187500075","https://openalex.org/W2167440101","https://openalex.org/W2150085486","https://openalex.org/W2126100045","https://openalex.org/W2016608818","https://openalex.org/W1523234081"],"abstract_inverted_index":{"In":[0],"the":[1,7,11,36,46,49,54,58,79,93,97,105,114,121,137,141,160,182],"area":[2],"of":[3,10,81,86,96],"face":[4,169,194],"recognition":[5,189,195],"research,":[6],"high":[8],"dimensionality":[9],"data":[12],"is":[13,117],"indeed":[14],"a":[15,21],"crucial":[16],"problem.":[17],"This":[18],"paper":[19],"proposes":[20],"new":[22],"fuzzy":[23,37,62,125],"and":[24,38,43,57,63,73,90,167],"Gaussian":[25,39,64],"distribution":[26,40],"induced":[27,66],"two-directional":[28],"inverse":[29,98,107],"Fisher's":[30,99],"discriminant":[31],"analysis":[32],"(FGD-2DIFDA)":[33],"which":[34],"computes":[35],"membership":[41,67,132,147],"values":[42,47,68,133],"thereby":[44],"combined":[45],"with":[48],"training":[50],"samples":[51],"to":[52,103,149],"obtain":[53,187],"class-wise":[55],"mean":[56],"global":[59],"mean.":[60],"These":[61],"function":[65],"are":[69,101,134],"incorporated":[70],"in":[71,113,136],"inter-":[72],"intra-class":[74],"scatter":[75],"matrices":[76],"for":[77],"diminishing":[78],"effect":[80],"uncertainty":[82,111],"formed":[83],"by":[84,120],"variation":[85],"light":[87],"facial":[88],"expression":[89],"pose.":[91],"Finally,":[92],"eigenvalue":[94],"problems":[95],"criteria":[100],"solved":[102],"finds":[104],"optimal":[106],"projection":[108],"vectors.":[109],"The":[110,153],"associated":[112],"image":[115],"region":[116],"efficiently":[118],"managed":[119],"FGD-2DIFDA":[122,154,184],"method":[123,129,155,185],"than":[124,191],"generalised":[126],"2DFLD":[127],"(FG-2DFLD)":[128],"as":[130,164,176],"two":[131],"employed":[135],"former":[138],"case":[139],"while":[140],"later":[142],"one":[143,146],"uses":[144],"only":[145],"value":[148],"handle":[150],"such":[151],"situation.":[152],"has":[156],"been":[157],"evaluated":[158],"on":[159],"AT&T":[161],"(formally":[162],"known":[163],"ORL),":[165],"UMIST":[166],"FERET":[168],"databases":[170],"using":[171],"support":[172],"vector":[173],"machine":[174],"(SVM)":[175],"classifier.":[177],"Simulation":[178],"results":[179],"demonstrate":[180],"that":[181],"proposed":[183],"can":[186],"higher":[188],"rate":[190],"some":[192],"state-of-the-art":[193],"methods.":[196]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4293202356","counts_by_year":[],"updated_date":"2024-12-11T14:23:09.495240","created_date":"2022-08-27"}