{"id":"https://openalex.org/W4386893918","doi":"https://doi.org/10.1142/s0218213023500689","title":"Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model","display_name":"Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model","publication_year":2023,"publication_date":"2023-09-20","ids":{"openalex":"https://openalex.org/W4386893918","doi":"https://doi.org/10.1142/s0218213023500689"},"language":"en","primary_location":{"id":"doi:10.1142/s0218213023500689","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213023500689","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"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":"International Journal on Artificial Intelligence Tools","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/A5086411965","display_name":"Tejaswini Yadav","orcid":"https://orcid.org/0009-0006-3368-6186"},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Tejaswini Yadav","raw_affiliation_strings":["Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra 412201, India"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra 412201, India","institution_ids":["https://openalex.org/I4210162439"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015589843","display_name":"Rajneeshkaur Sachdeo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162439","display_name":"MIT Art, Design and Technology University","ror":"https://ror.org/05b69xa56","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210162439"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajneeshkaur Sachdeo","raw_affiliation_strings":["Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra 412201, India"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra 412201, India","institution_ids":["https://openalex.org/I4210162439"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086411965"],"corresponding_institution_ids":["https://openalex.org/I4210162439"],"apc_list":null,"apc_paid":null,"fwci":0.2469,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52861252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"32","issue":"07","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9790999889373779,"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/T11448","display_name":"Face recognition and analysis","score":0.9790999889373779,"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/T13169","display_name":"Consumer Perception and Purchasing Behavior","score":0.9394999742507935,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.816189169883728},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6485815644264221},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5643676519393921},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5582425594329834},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.5133134722709656},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4454784095287323},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.44218388199806213},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41125333309173584},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34249359369277954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2983196973800659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.816189169883728},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6485815644264221},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5643676519393921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5582425594329834},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.5133134722709656},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4454784095287323},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.44218388199806213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41125333309173584},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34249359369277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2983196973800659},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218213023500689","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213023500689","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"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":"International Journal on Artificial Intelligence Tools","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1839137930","https://openalex.org/W1978370894","https://openalex.org/W2020447345","https://openalex.org/W2105026179","https://openalex.org/W2118755929","https://openalex.org/W2126972425","https://openalex.org/W2162593906","https://openalex.org/W2227430255","https://openalex.org/W2470405576","https://openalex.org/W2614580201","https://openalex.org/W2726140679","https://openalex.org/W2735913953","https://openalex.org/W2748543394","https://openalex.org/W2891174944","https://openalex.org/W2893531431","https://openalex.org/W2898088959","https://openalex.org/W2922263752","https://openalex.org/W2944730324","https://openalex.org/W2962679668","https://openalex.org/W2965713809","https://openalex.org/W3028007091","https://openalex.org/W3054464910","https://openalex.org/W3100819185","https://openalex.org/W3120683219","https://openalex.org/W3128902831","https://openalex.org/W3133100792","https://openalex.org/W3138753100","https://openalex.org/W3153929263","https://openalex.org/W3164010852","https://openalex.org/W3164956625","https://openalex.org/W3174422331","https://openalex.org/W4225130320","https://openalex.org/W4234465742","https://openalex.org/W4251442645","https://openalex.org/W4252752406","https://openalex.org/W4285192846"],"related_works":["https://openalex.org/W1967587236","https://openalex.org/W2098693229","https://openalex.org/W2384651879","https://openalex.org/W2336272890","https://openalex.org/W4308999381","https://openalex.org/W3183843611","https://openalex.org/W4312238398","https://openalex.org/W3211418293","https://openalex.org/W4308999963","https://openalex.org/W2133653344"],"abstract_inverted_index":{"Face":[0,24],"age":[1,77,125,138,150,190,245],"progression":[2,139,191,200],"aims":[3],"to":[4,14,51,89,182,310],"change":[5],"an":[6,248],"individual\u2019s":[7],"face":[8,12,48,76,124,137,156,189,199,205,244],"from":[9],"a":[10,38,106],"provided":[11],"image":[13,18,94,220,232],"forecast":[15],"how":[16],"that":[17,321],"will":[19],"look":[20],"in":[21,30,93,145],"the":[22,53,65,69,71,90,147,159,166,169,184,194,198,204,209,215,218,231,235,250,253,261,276,281,300,304,317,322],"future.":[23],"aging":[25,79],"is":[26,141,206,221,233,241,286,303],"gaining":[27],"much":[28],"attention":[29],"today\u2019s":[31],"environment,":[32],"which":[33],"needs":[34,74],"better":[35],"security":[36],"and":[37,81,101,121,140,143,203,226,289,295,328],"touchless":[39],"unique":[40],"identification":[41],"mechanism.":[42],"Researchers":[43],"are":[44,84,201,256],"focused":[45],"on":[46,105,136],"creating":[47],"processing":[49],"algorithms":[50],"address":[52],"difficulty":[54],"of":[55,75,109,132,158,168,217,252,278,284],"producing":[56],"realistic":[57],"aged":[58],"faces":[59],"for":[60,197,243],"smart":[61],"system":[62],"applications":[63,111],"over":[64],"earlier":[66],"decades.":[67],"In":[68],"literature,":[70],"two":[72],"basic":[73],"progression,":[78],"accuracy,":[80,327],"identity":[82],"preservation":[83],"not":[85],"thoroughly":[86],"addressed.":[87],"According":[88],"extraordinary":[91],"gains":[92],"synthesis":[95],"made":[96],"by":[97,173,223,260],"deep":[98],"generative":[99],"methods":[100],"their":[102],"significant":[103],"influence":[104],"wide":[107],"variety":[108],"practical":[110],"such":[112],"as":[113,268],"identifying":[114],"missing":[115],"persons":[116],"using":[117,208],"entertainment,":[118],"childhood":[119],"images,":[120],"so":[122],"on,":[123],"progression/regression":[126],"has":[127],"reawakened":[128],"attention.":[129],"The":[130],"majority":[131],"present":[133],"techniques":[134],"concentrate":[135],"beneficial":[142],"productive":[144],"learning":[146],"transition":[148],"across":[149],"groups":[151],"utilizing":[152,313],"paired":[153],"data,":[154],"i.e.,":[155],"images":[157],"similar":[160],"individual":[161],"at":[162],"various":[163],"ages.":[164],"Through":[165],"motivation":[167],"important":[170],"success":[171],"attained":[172],"Generative":[174],"Adversarial":[175],"Networks":[176],"(GANs),":[177],"this":[178],"paper":[179],"uses":[180],"tactics":[181],"implement":[183],"improved":[185],"Cycle":[186,254],"GAN-based":[187],"intelligent":[188],"model.":[192],"Initially,":[193],"standard":[195,315],"datasets":[196],"gathered,":[202],"detected":[207],"Viola-Jones":[210],"object":[211],"detection":[212],"algorithm.":[213],"Then,":[214],"pre-processing":[216],"facial":[219],"performed":[222],"median":[224],"filtering":[225],"contrast":[227],"enhancement":[228],"techniques.":[229],"Once":[230],"pre-processed,":[234],"Hyperparameter":[236],"Tuning-Cycle":[237],"GAN":[238,255],"(HT-Cycle":[239],"GAN)":[240],"adopted":[242],"progression.":[246],"As":[247],"improvement,":[249],"hyperparameters":[251],"optimized":[257],"or":[258],"tuned":[259],"modified":[262],"Galactic":[263,271],"Swarm":[264,272],"Optimization":[265,273],"(GSO),":[266],"known":[267],"Best":[269],"Fitness-based":[270],"(BF-GSO).":[274],"From":[275],"evaluation":[277],"statistical":[279],"analysis,":[280],"similarity":[282],"score":[283],"BF-GSO-HT-CycleGAN":[285],"0.80%,":[287],"3.33%,":[288],"2.86%":[290],"higher":[291],"than":[292],"cGAN,":[293],"CycleGAN,":[294],"Dubbed":[296,301],"FaceGAN,":[297],"respectively.":[298],"Here,":[299],"FaceGAN":[302],"2nd":[305],"greatest":[306],"network.":[307],"Furthermore,":[308],"compared":[309],"traditional":[311],"models":[312],"distinct":[314],"datasets,":[316],"experimental":[318],"findings":[319],"show":[320],"suggested":[323],"technique":[324],"attains":[325],"efficiency,":[326],"flexibility.":[329]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
