{"id":"https://openalex.org/W1786109841","doi":"https://doi.org/10.3233/ifs-120717","title":"Classification of driver fatigue expressions by combined curvelet features and gabor features, and random subspace ensembles of support vector machines","display_name":"Classification of driver fatigue expressions by combined curvelet features and gabor features, and random subspace ensembles of support vector machines","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W1786109841","doi":"https://doi.org/10.3233/ifs-120717","mag":"1786109841"},"language":"en","primary_location":{"id":"doi:10.3233/ifs-120717","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ifs-120717","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","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/A5087300333","display_name":"Chihang Zhao","orcid":"https://orcid.org/0000-0003-0315-4796"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chihang Zhao","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China","College of Transportation, Southeast University, Nanjing, PR China#TAB#"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China#TAB#","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101618323","display_name":"Jie Lian","orcid":"https://orcid.org/0000-0002-9752-8260"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Lian","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China","College of Transportation, Southeast University, Nanjing, PR China#TAB#"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China#TAB#","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104101741","display_name":"Qian Dang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Dang","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China","College of Transportation, Southeast University, Nanjing, PR China#TAB#"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China#TAB#","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102923405","display_name":"Can Tong","orcid":"https://orcid.org/0000-0002-1446-3810"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Can Tong","raw_affiliation_strings":["College of Transportation, Southeast University, Nanjing, PR China","College of Transportation, Southeast University, Nanjing, PR China#TAB#"],"affiliations":[{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"College of Transportation, Southeast University, Nanjing, PR China#TAB#","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087300333"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":1.8666,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84521623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"26","issue":"1","first_page":"91","last_page":"100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9987999796867371,"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":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.856738805770874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7181973457336426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6975380778312683},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6723831295967102},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6632986068725586},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.6561784148216248},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6176099181175232},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.5924602746963501},{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.48330163955688477},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.474293977022171},{"id":"https://openalex.org/keywords/gabor-wavelet","display_name":"Gabor wavelet","score":0.4457811713218689},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3005889654159546},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2809399366378784},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.2636171877384186},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.2476048767566681},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.12303751707077026}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.856738805770874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7181973457336426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6975380778312683},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6723831295967102},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6632986068725586},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.6561784148216248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6176099181175232},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.5924602746963501},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.48330163955688477},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.474293977022171},{"id":"https://openalex.org/C136902061","wikidata":"https://www.wikidata.org/wiki/Q16981559","display_name":"Gabor wavelet","level":5,"score":0.4457811713218689},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3005889654159546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2809399366378784},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.2636171877384186},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.2476048767566681},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.12303751707077026},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ifs-120717","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ifs-120717","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","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":40,"referenced_works":["https://openalex.org/W601322659","https://openalex.org/W801264600","https://openalex.org/W1484322501","https://openalex.org/W1485280399","https://openalex.org/W1514958855","https://openalex.org/W1972175688","https://openalex.org/W1988790447","https://openalex.org/W1991919071","https://openalex.org/W1995819460","https://openalex.org/W2005305331","https://openalex.org/W2012713115","https://openalex.org/W2029772767","https://openalex.org/W2030909430","https://openalex.org/W2052604480","https://openalex.org/W2052770734","https://openalex.org/W2077563416","https://openalex.org/W2085986654","https://openalex.org/W2091984081","https://openalex.org/W2092728879","https://openalex.org/W2093449404","https://openalex.org/W2113242816","https://openalex.org/W2115528090","https://openalex.org/W2119821739","https://openalex.org/W2124868070","https://openalex.org/W2125148312","https://openalex.org/W2130012476","https://openalex.org/W2132680427","https://openalex.org/W2143593953","https://openalex.org/W2146182319","https://openalex.org/W2147652878","https://openalex.org/W2147924864","https://openalex.org/W2148603752","https://openalex.org/W2152137105","https://openalex.org/W2153104898","https://openalex.org/W2157124852","https://openalex.org/W2487087946","https://openalex.org/W2591127855","https://openalex.org/W2912934387","https://openalex.org/W2973818247","https://openalex.org/W3097096317"],"related_works":["https://openalex.org/W2047865552","https://openalex.org/W2353199197","https://openalex.org/W2357150443","https://openalex.org/W127070535","https://openalex.org/W2382704364","https://openalex.org/W2384322977","https://openalex.org/W2140869420","https://openalex.org/W2393746448","https://openalex.org/W3013206934","https://openalex.org/W4221107122"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,12,163],"develop":[3],"Human-centric":[4,158],"Driver":[5,159],"Fatigue":[6,160],"Monitoring":[7,161],"Systems":[8,162],"(HDFMS)":[9],"with":[10,47,89,119],"aims":[11],"increase":[13,164],"driving":[14,165],"safety,":[15],"an":[16],"efficient":[17],"combined":[18,146],"features":[19,98,103,147],"extraction":[20,148],"from":[21],"Curvelet":[22,97],"transform":[23,27],"and":[24,37,71,79,115,137,149],"Gabor":[25,102],"wavelet":[26],"for":[28,57,156],"fatigue":[29,62,69,73,127],"expressions":[30,63,128],"descriptions":[31],"of":[32,42,59,77,87,117,125,134,145,151],"vehicle":[33],"drivers":[34],"is":[35,54],"proposed,":[36],"Random":[38],"Subspace":[39],"Ensemble":[40],"(RSE)":[41],"Support":[43],"Vector":[44],"Machines":[45],"(SVMs)":[46],"polynomial":[48,90,120],"kernel":[49,91],"as":[50],"the":[51,122,135,142],"base":[52],"classifier":[53],"then":[55],"exploited":[56],"classification":[58,123],"three":[60],"predefined":[61],"classes,":[64],"namely,":[65],"awake":[66],"expressions,":[67,70],"moderate":[68],"severe":[72],"expressions.":[74],"The":[75],"results":[76],"holdout":[78,136],"cross-validation":[80,138],"experiments":[81],"show":[82,141],"that":[83],"CF":[84,107,114],"by":[85,99,104,108],"RSE":[86,116,150],"SVMs":[88,100,105,111,118,152],"outperforms":[92],"other":[93],"seven":[94],"classifiers,":[95],"i.e.,":[96],"classifier,":[101,106],"five":[109],"individual":[110],"classifiers.":[112],"With":[113],"kernel,":[121],"accuracies":[124],"drivers'":[126],"are":[129],"over":[130],"90%":[131],"in":[132],"both":[133],"experiments,":[139],"which":[140],"proposed":[143],"approach":[144],"can":[153],"be":[154],"used":[155],"developing":[157],"safety.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
