{"id":"https://openalex.org/W2910468746","doi":"https://doi.org/10.1109/aiccsa.2018.8612896","title":"Improving Human Face Recognition using Deep Learning based Image Registration and Multi-Classifier Approaches","display_name":"Improving Human Face Recognition using Deep Learning based Image Registration and Multi-Classifier Approaches","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2910468746","doi":"https://doi.org/10.1109/aiccsa.2018.8612896","mag":"2910468746"},"language":"en","primary_location":{"id":"doi:10.1109/aiccsa.2018.8612896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa.2018.8612896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","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/A5090472246","display_name":"Mohannad Abuzneid","orcid":"https://orcid.org/0000-0002-1478-7436"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohannad Abuzneid","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, USA","institution_ids":["https://openalex.org/I154300980"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005346248","display_name":"Ausif Mahmood","orcid":"https://orcid.org/0000-0002-8991-4268"},"institutions":[{"id":"https://openalex.org/I154300980","display_name":"University of Bridgeport","ror":"https://ror.org/01rf3yp57","country_code":"US","type":"education","lineage":["https://openalex.org/I154300980"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ausif Mahmood","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, USA","institution_ids":["https://openalex.org/I154300980"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154300980"],"apc_list":null,"apc_paid":null,"fwci":0.318,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.64647995,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9991999864578247,"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":0.9991999864578247,"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/T10057","display_name":"Face and Expression Recognition","score":0.9958000183105469,"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/T10828","display_name":"Biometric Identification and Security","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.829757571220398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8052411079406738},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.7291595935821533},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6350418329238892},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6007575988769531},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5919158458709717},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5723334550857544},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5563153028488159},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.518543541431427},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.47530731558799744},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4574441611766815},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43941304087638855},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41238296031951904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.829757571220398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8052411079406738},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.7291595935821533},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6350418329238892},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6007575988769531},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5919158458709717},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5723334550857544},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5563153028488159},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.518543541431427},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.47530731558799744},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4574441611766815},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43941304087638855},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41238296031951904},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/aiccsa.2018.8612896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa.2018.8612896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarworks.bridgeport.edu:123456789/3968","is_oa":false,"landing_page_url":"https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3968","pdf_url":null,"source":{"id":"https://openalex.org/S4306400238","display_name":"University of Bridgeport ScholarWorks (University of Bridgeport)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154300980","host_organization_name":"University of Bridgeport","host_organization_lineage":["https://openalex.org/I154300980"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2126145365","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W2388958958","https://openalex.org/W2985118265","https://openalex.org/W598569869"],"abstract_inverted_index":{"Face":[0],"recognition":[1,72,169,227,289],"has":[2],"become":[3],"a":[4,52,74,100,131,141,185,196,286],"fascinating":[5],"field":[6],"for":[7,56],"researchers.":[8],"The":[9,29,137],"motivation":[10],"behind":[11],"the":[12,16,19,23,32,36,84,87,97,104,107,116,120,123,126,146,151,167,177,215,221,235,245,252,258,273,280,299,318,323],"enormous":[13],"interest":[14],"in":[15,166,279],"topic":[17],"is":[18,96,112,164,195],"need":[20],"to":[21,39,46,68,114,153,191,213,234,297,326],"improve":[22,69,298],"accuracy":[24,228],"of":[25,31,93,99,134,272],"many":[26],"real-time":[27],"applications.":[28],"complexity":[30],"human":[33,57,70],"face":[34,58,71,168,226,288,300],"and":[35,79,156,161,171,209,254,313,330],"changes":[37],"due":[38],"different":[40],"effects":[41],"make":[42],"it":[43],"more":[44],"challenging":[45],"design":[47],"as":[48,50,257,311],"well":[49],"implement":[51],"powerful":[53],"computational":[54,231],"system":[55],"recognition.":[59],"In":[60],"this":[61,94],"work,":[62],"we":[63,183,218,266,284,292,331],"presented":[64],"an":[65,294],"enhanced":[66,295],"approach":[67,237],"using":[73,130,200,239,302],"Back-Propagation":[75],"Neural":[76,179],"Network":[77,180],"(BPNN)":[78],"features":[80,162],"extraction":[81],"based":[82],"on":[83,176,248,270,317],"correlation":[85,124],"between":[86,125,145],"training":[88,109,127,147],"images.":[89],"A":[90],"key":[91],"contribution":[92],"work":[95],"generation":[98],"new":[101],"set":[102],"called":[103],"T-Dataset":[105,121,139],"from":[106],"original":[108],"dataset,":[110],"which":[111,149,217,315],"used":[113,184,307,322],"train":[115],"BPNN.":[117,222],"We":[118,203,223,243,261,306,321],"generated":[119],"utilizing":[122],"images":[128],"without":[129],"conventional":[132],"technique":[133],"image":[135,241],"density.":[136],"correlated":[138],"provides":[140],"high":[142,333],"distinction":[143],"layer":[144],"images,":[148],"helps":[150],"BPNN":[152],"converge":[154],"faster":[155],"achieve":[157,332],"better":[158],"accuracy.":[159,264,334],"Data":[160],"reduction":[163],"essential":[165],"process,":[170],"researchers":[172],"have":[173],"recently":[174],"focused":[175],"modern":[178],"(NN).":[181],"Therefore,":[182],"Principal":[186],"Component":[187],"Analysis":[188],"(PCA)":[189],"descriptor":[190],"prove":[192],"that":[193],"there":[194],"potential":[197],"improvement":[198],"even":[199],"traditional":[201],"methods.":[202],"applied":[204],"five":[205],"distance":[206],"measurement":[207],"algorithms":[208],"then":[210],"combined":[211],"them":[212],"obtain":[214],"T-Dataset,":[216],"fed":[219],"into":[220],"achieved":[224,262],"higher":[225],"with":[229],"less":[230],"cost":[232],"compared":[233],"current":[236],"by":[238],"reduced":[240],"features.":[242],"test":[244],"proposed":[246,293],"framework":[247,296],"two":[249],"small":[250],"datasets,":[251,256,276],"YALE":[253],"AT&T":[255],"ground":[259],"truth.":[260],"tremendous":[263],"Furthermore,":[265],"evaluate":[267,327],"our":[268,328],"method":[269],"one":[271],"state-of-the-art":[274],"benchmark":[275],"Labeled":[277],"Faces":[278],"Wild":[281],"(LFW),":[282],"where":[283],"produce":[285],"competitive":[287],"performance.":[290],"Also,":[291],"registration":[301],"deep":[303],"transfer":[304],"learning.":[305],"pre-trained":[308],"models":[309],"such":[310],"ResNet50":[312],"SeNet50":[314],"trained":[316],"VGGFace2":[319],"dataset.":[320],"LFW":[324],"dataset":[325],"method,":[329]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}