{"id":"https://openalex.org/W4381327845","doi":"https://doi.org/10.1109/access.2023.3288008","title":"Facial Feature-Based Drowsiness Detection With Multi-Scale Convolutional Neural Network","display_name":"Facial Feature-Based Drowsiness Detection With Multi-Scale Convolutional Neural Network","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4381327845","doi":"https://doi.org/10.1109/access.2023.3288008"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3288008","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3288008","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10158440.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10158440.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023552618","display_name":"V. Vijaypriya","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"V. Vijaypriya","raw_affiliation_strings":["Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India"],"raw_orcid":"https://orcid.org/0000-0002-1181-2763","affiliations":[{"raw_affiliation_string":"Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019008409","display_name":"Uma M","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohan Uma","raw_affiliation_strings":["Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India"],"raw_orcid":"https://orcid.org/0000-0003-0976-7411","affiliations":[{"raw_affiliation_string":"Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023552618"],"corresponding_institution_ids":["https://openalex.org/I145286018"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":8.3407,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.98189283,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"11","issue":null,"first_page":"63417","last_page":"63429"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9236999750137329,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7875034809112549},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7793611884117126},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7405054569244385},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7243080735206604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6087632775306702},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4810102880001068},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47311922907829285},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4204854965209961},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4186268150806427},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.41528022289276123}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7875034809112549},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7793611884117126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7405054569244385},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7243080735206604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6087632775306702},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4810102880001068},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47311922907829285},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4204854965209961},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4186268150806427},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.41528022289276123},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3288008","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3288008","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10158440.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c0f03be2445244a08330819d1a57b0a4","is_oa":true,"landing_page_url":"https://doaj.org/article/c0f03be2445244a08330819d1a57b0a4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 63417-63429 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3288008","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3288008","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10158440.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4381327845.pdf","grobid_xml":"https://content.openalex.org/works/W4381327845.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W2101956459","https://openalex.org/W2605351369","https://openalex.org/W2605945429","https://openalex.org/W2802132828","https://openalex.org/W2979979528","https://openalex.org/W2980729422","https://openalex.org/W3001630243","https://openalex.org/W3033750148","https://openalex.org/W3044774336","https://openalex.org/W3045226817","https://openalex.org/W3084858064","https://openalex.org/W3106596522","https://openalex.org/W3120816842","https://openalex.org/W3122582437","https://openalex.org/W3130054339","https://openalex.org/W3130409332","https://openalex.org/W3133565772","https://openalex.org/W3155738052","https://openalex.org/W3158433777","https://openalex.org/W3164743527","https://openalex.org/W3175738057","https://openalex.org/W3194411726","https://openalex.org/W3195353695","https://openalex.org/W3201296683","https://openalex.org/W3210869680","https://openalex.org/W3215237634","https://openalex.org/W4200550203","https://openalex.org/W4206066711","https://openalex.org/W4207012543","https://openalex.org/W4210707449","https://openalex.org/W4220818737","https://openalex.org/W4224882656","https://openalex.org/W4283077725","https://openalex.org/W4285606031","https://openalex.org/W4289278060","https://openalex.org/W4289527090","https://openalex.org/W4306318675","https://openalex.org/W4313241637","https://openalex.org/W4319971326","https://openalex.org/W4320717079"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Recently,":[0],"the":[1,48,71,76,98,104,112,118,128,135,148,151,155,165,172,182,201,228,238],"upsurge":[2],"in":[3,44,117,154],"accidents":[4,40],"is":[5,36,51,82,93,109],"caused":[6,41],"due":[7,12],"to":[8,13,25,38],"driver":[9],"drowsiness":[10,23,43,55],"arises":[11],"lack":[14],"of":[15,28,73,114,150,210,223,227],"sleep,":[16],"fatigue":[17],"and":[18,30,56,79,101,192,216],"other":[19],"health":[20,32],"factors.":[21],"The":[22,90,121,159,196,225],"leads":[24],"mortality,":[26],"loss":[27],"properties":[29],"serious":[31],"conditions.":[33],"Hence,":[34],"it":[35],"necessary":[37],"prevent":[39],"by":[42,134],"drivers.":[45],"At":[46],"present,":[47],"automated":[49],"model":[50,175,188,205,218,231],"effective":[52],"for":[53,70,84,97,111,147,213],"detection":[54],"recognition.":[57],"In":[58],"this":[59],"research":[60],"paper,":[61],"developed":[62],"a":[63],"MCNN":[64,185,202,230],"(Multi-Scale":[65],"Convolutional":[66],"Neural":[67],"Network)":[68],"framework":[69],"classification":[72],"drowsiness.":[74],"Initially,":[75],"YAWDD":[77,214],"dataset":[78,81,215],"NTHU-DDD":[80,217],"utilized":[83,110],"acquiring":[85],"video":[86,92],"sequences":[87],"about":[88],"driving.":[89],"acquired":[91],"converted":[94],"into":[95],"frames":[96,124],"keyframe":[99],"extraction":[100,137,149,190],"selection.":[102],"With":[103,181],"Dlib":[105],"library":[106],"face":[107],"recognition":[108],"localization":[113],"facial":[115],"points":[116,161],"extracted":[119,122],"frames.":[120],"image":[123,156],"are":[125,162,194],"pre-processed":[126],"with":[127,138,144,164,171,203],"Cross":[129],"Guided":[130],"Bilateral":[131],"Filtering":[132],"followed":[133],"feature":[136,152,160,189],"hybrid":[139],"dual-tree":[140],"complex":[141],"wavelet":[142],"transforms":[143],"Walsh-Hadamard":[145],"transform":[146],"vector":[153],"frame":[157],"blocks.":[158],"optimized":[163],"Flamingo":[166],"search":[167],"algorithm":[168],"(FSA)":[169],"integrated":[170],"deep":[173],"learning":[174],"Multiscale":[176],"convolutional":[177],"neural":[178],"network":[179],"(MCNN).":[180],"proposed":[183,197,229],"method,":[184],"based":[186],"FSA":[187,204],"drowsy":[191],"non-drowsy":[193],"classified.":[195],"simulation":[198],"results":[199],"illustrated":[200],"attains":[206],"an":[207,220],"accuracy":[208,221,236],"value":[209,222],"around":[211],"98.38%":[212],"exhibits":[219,232],"98.26%.":[224],"performance":[226],"approximately":[233],"6%":[234],"higher":[235],"than":[237],"conventional":[239],"state-of-art":[240],"methods.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2025-10-10T00:00:00"}
