{"id":"https://openalex.org/W4286285635","doi":"https://doi.org/10.1109/iv51971.2022.9827229","title":"Driver\u2019s Drowsiness Classifier using a Single-Camera Robust to Mask-wearing Situations using an Eyelid, Lower-Face Contour, and Chest Movement Feature Vector GRU-based Model","display_name":"Driver\u2019s Drowsiness Classifier using a Single-Camera Robust to Mask-wearing Situations using an Eyelid, Lower-Face Contour, and Chest Movement Feature Vector GRU-based Model","publication_year":2022,"publication_date":"2022-06-05","ids":{"openalex":"https://openalex.org/W4286285635","doi":"https://doi.org/10.1109/iv51971.2022.9827229"},"language":"en","primary_location":{"id":"doi:10.1109/iv51971.2022.9827229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827229","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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/A5061983945","display_name":"Catherine Lollett","orcid":"https://orcid.org/0000-0001-7478-423X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Catherine Lollett","raw_affiliation_strings":["Waseda University,Graduate School of Creative Science and Engineering,Department of Modern Mechanical Engineering,17 Kikui-cho, Shinjuku-ku, Tokyo,Japan,162-0044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Creative Science and Engineering,Department of Modern Mechanical Engineering,17 Kikui-cho, Shinjuku-ku, Tokyo,Japan,162-0044","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001851501","display_name":"Mitsuhiro Kamezaki","orcid":"https://orcid.org/0000-0002-4377-8993"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mitsuhiro Kamezaki","raw_affiliation_strings":["Research Institute for Science and Engineering (RISE), Waseda University,17 Kikui-cho, Shinjuku-ku, Tokyo,Japan,162-0044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute for Science and Engineering (RISE), Waseda University,17 Kikui-cho, Shinjuku-ku, Tokyo,Japan,162-0044","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080654277","display_name":"Shigeki Sugano","orcid":"https://orcid.org/0000-0002-9331-2446"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeki Sugano","raw_affiliation_strings":["Waseda University,Department of Modern Mechanical Engineering,3-4-1 Okubo, Shinjuku-ku, Tokyo,Japan,169-8555"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Department of Modern Mechanical Engineering,3-4-1 Okubo, Shinjuku-ku, Tokyo,Japan,169-8555","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9998999834060669,"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":0.9998999834060669,"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/T12006","display_name":"Ergonomics and Musculoskeletal Disorders","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.906499981880188,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8170756697654724},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6930878162384033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.680991530418396},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.6376442313194275},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5184352397918701},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.4751672148704529},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44297346472740173},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.43627044558525085},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4207553267478943},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4119718670845032},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08865770697593689}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8170756697654724},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6930878162384033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680991530418396},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.6376442313194275},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5184352397918701},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.4751672148704529},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44297346472740173},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.43627044558525085},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4207553267478943},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4119718670845032},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08865770697593689},{"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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv51971.2022.9827229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827229","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8742046876","display_name":"Control method for interface considering flexible collaboration on embodiment","funder_award_id":"20H00616","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320307930","display_name":"Interface","ror":"https://ror.org/04nvtmr42"},{"id":"https://openalex.org/F4320322638","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W207434894","https://openalex.org/W1508616463","https://openalex.org/W1585406901","https://openalex.org/W1834627138","https://openalex.org/W1978602582","https://openalex.org/W1996689795","https://openalex.org/W2049853010","https://openalex.org/W2052770734","https://openalex.org/W2058333183","https://openalex.org/W2058569601","https://openalex.org/W2072740614","https://openalex.org/W2093449404","https://openalex.org/W2101956459","https://openalex.org/W2120598715","https://openalex.org/W2126074213","https://openalex.org/W2134738818","https://openalex.org/W2356490583","https://openalex.org/W2575714833","https://openalex.org/W2906548844","https://openalex.org/W2943512776","https://openalex.org/W2963307811","https://openalex.org/W2963708869","https://openalex.org/W2970967525","https://openalex.org/W2995744782","https://openalex.org/W2997052735","https://openalex.org/W3024582231","https://openalex.org/W3145697163","https://openalex.org/W3173340449","https://openalex.org/W3194495309","https://openalex.org/W6630379850","https://openalex.org/W6638891565","https://openalex.org/W6732337687","https://openalex.org/W6743555153","https://openalex.org/W6748370023","https://openalex.org/W6793244959"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W4243161226","https://openalex.org/W2950647290","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W1968481813","https://openalex.org/W2392886708"],"abstract_inverted_index":{"Drowsy":[0],"drivers":[1,116],"cause":[2],"many":[3],"deadly":[4],"crashes.":[5],"As":[6,93],"a":[7,61,65,88,94,99,138,214,222],"result,":[8,95],"researchers":[9],"focus":[10],"on":[11],"using":[12,148,196,213],"driver":[13,59,89],"drowsiness":[14,114,237],"classifiers":[15],"to":[16,112,130,171,199],"predict":[17],"this":[18,32,96],"condition":[19],"in":[20,115,239],"advance.":[21],"However,":[22,50],"they":[23],"only":[24],"consider":[25,41,181],"constraint":[26],"situations.":[27,119],"Under":[28],"highly":[29,225],"unrestricted":[30],"scenarios,":[31],"categorization":[33],"remains":[34],"extremely":[35],"difficult.":[36],"For":[37],"example,":[38],"several":[39,192],"studies":[40],"the":[42,51,58,123,128,142,173,201,235],"driver\u2019s":[43,236],"mouth":[44,52],"closure":[45,53],"crucial":[46],"for":[47,68,158],"detecting":[48],"drowsiness.":[49],"cannot":[54],"be":[55],"seen":[56],"when":[57],"wears":[60],"mask,":[62],"which":[63],"is":[64],"potential":[66],"failure":[67],"these":[69,72,197],"classifiers.":[70],"Moreover,":[71],"works":[73],"do":[74],"not":[75],"make":[76],"experiments":[77],"under":[78,117,244],"unconstrained":[79,118,226],"situations":[80],"as":[81],"environments":[82],"with":[83,90,224],"considerable":[84],"light":[85,133],"variation":[86],"or":[87],"eyeglasses":[91],"reflections.":[92],"paper":[97],"proposes":[98],"video-based":[100],"novel":[101],"pipeline":[102],"that":[103,152,230],"employs":[104],"new":[105],"parameters,":[106],"computer":[107],"vision":[108],"and":[109,145,161,166,188,194],"deep-learning":[110],"techniques":[111],"identify":[113],"First,":[120],"we":[121,136,180,209],"alter":[122],"Lab":[124],"color":[125],"space":[126],"of":[127,141,175,203],"frame":[129],"ease":[131],"strong":[132],"changes.":[134],"Then,":[135],"achieve":[137],"robust":[139],"recognition":[140],"face,":[143],"eyes":[144],"body-joints":[146],"landmarks":[147,198],"dense":[149],"landmark":[150],"detection":[151],"includes":[153],"optical":[154],"flow":[155],"estimation":[156],"methods":[157],"3D":[159],"eyelid":[160],"facial":[162],"expression":[163],"movement":[164],"tracking":[165],"an":[167],"online":[168],"optimization":[169],"framework":[170],"build":[172],"association":[174],"cross-frame":[176],"poses.":[177],"After":[178],"this,":[179],"three":[182,204],"important":[183],"landmarks:":[184],"eyes,":[185],"lower-face":[186],"contour,":[187],"chest.":[189],"We":[190],"performed":[191],"pre-processing":[193],"combinations":[195],"compare":[200],"efficiency":[202],"alternative":[205],"feature":[206],"vectors.":[207],"Finally,":[208],"fuse":[210],"spatiotemporal":[211],"features":[212],"Gated":[215],"Recurrent":[216],"Units":[217],"(GRU)":[218],"model.":[219],"Results":[220],"over":[221],"dataset":[223],"driving":[227],"conditions":[228],"demonstrate":[229],"our":[231],"method":[232],"outperforms":[233],"classifying":[234],"correctly":[238],"various":[240],"challenging":[241],"situations,":[242],"all":[243],"mask-wearing":[245],"scenarios.":[246]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
