{"id":"https://openalex.org/W3042738572","doi":"https://doi.org/10.1109/comm48946.2020.9142020","title":"An Ensemble of Deep Convolutional Neural Networks for Drunkenness Detection Using Thermal Infrared Facial Imagery","display_name":"An Ensemble of Deep Convolutional Neural Networks for Drunkenness Detection Using Thermal Infrared Facial Imagery","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3042738572","doi":"https://doi.org/10.1109/comm48946.2020.9142020","mag":"3042738572"},"language":"en","primary_location":{"id":"doi:10.1109/comm48946.2020.9142020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm48946.2020.9142020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Conference on Communications (COMM)","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/A5050266750","display_name":"Victor-Emil Neagoe","orcid":"https://orcid.org/0000-0003-1586-6563"},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]},{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Victor-Emil Neagoe","raw_affiliation_strings":["Faculty of Electronics, Telecomm, & Inform. Technol., Polytechnic University of Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Faculty of Electronics, Telecomm, & Inform. Technol., Polytechnic University of Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I141595442","https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039351350","display_name":"Paul Diaconescu","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]},{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Paul Diaconescu","raw_affiliation_strings":["Polytechnic University of Bucharest,Faculty of Electronics, Telecomm, & Inform. Technol.,Bucharest,Romania"],"affiliations":[{"raw_affiliation_string":"Polytechnic University of Bucharest,Faculty of Electronics, Telecomm, & Inform. Technol.,Bucharest,Romania","institution_ids":["https://openalex.org/I141595442","https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050266750"],"corresponding_institution_ids":["https://openalex.org/I141595442","https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":0.7954,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.78228254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"147","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9800999760627747,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8015415668487549},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6452827453613281},{"id":"https://openalex.org/keywords/thermal-infrared","display_name":"Thermal infrared","score":0.644748866558075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6201112866401672},{"id":"https://openalex.org/keywords/infrared","display_name":"Infrared","score":0.5104288458824158},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5061497092247009},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40534162521362305},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0676206648349762},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.05286139249801636}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8015415668487549},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6452827453613281},{"id":"https://openalex.org/C2984335091","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Thermal infrared","level":3,"score":0.644748866558075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6201112866401672},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.5104288458824158},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5061497092247009},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40534162521362305},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0676206648349762},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.05286139249801636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comm48946.2020.9142020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm48946.2020.9142020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Conference on Communications (COMM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W12241386","https://openalex.org/W2066322905","https://openalex.org/W2075747571","https://openalex.org/W2095705004","https://openalex.org/W2097233305","https://openalex.org/W2102738574","https://openalex.org/W2115047029","https://openalex.org/W2122519597","https://openalex.org/W2134905716","https://openalex.org/W2323929895","https://openalex.org/W2559785631","https://openalex.org/W2592160412","https://openalex.org/W2742342491","https://openalex.org/W2919115771","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W3184332868","https://openalex.org/W2360806688","https://openalex.org/W273971055","https://openalex.org/W1858933348","https://openalex.org/W2351006740","https://openalex.org/W3157789538","https://openalex.org/W2372640428","https://openalex.org/W4390606651","https://openalex.org/W3040982946","https://openalex.org/W2907184496"],"abstract_inverted_index":{"This":[0,167],"paper":[1],"proposes":[2],"an":[3,12,39],"original":[4],"method":[5,97],"for":[6,20,45,130,153],"subject":[7,115,138,154],"independent":[8,155],"drunkenness":[9,156],"detection":[10,157,163],"using":[11,73,100],"ensemble":[13,40],"of":[14,22,38,41,79,90,103,144,148,165,171],"Deep":[15],"Convolutional":[16],"Neural":[17],"Networks":[18],"(DCNNs)":[19],"processing":[21],"thermal":[23,46,105,120],"infrared":[24,47,106],"facial":[25,48,107],"imagery":[26],"characterizing":[27],"the":[28,51,60,87,101,116,137,149,160,169,172],"subjects":[29],"to":[30,110,123,159],"be":[31],"tested.":[32],"The":[33,66,81,95,146],"proposed":[34,96,150,173],"neural":[35],"system":[36],"consists":[37],"two":[42,67,91],"DCNNs":[43,68],"modules":[44],"image":[49],"processing;":[50],"first":[52],"module":[53],"is":[54,84,98],"composed":[55],"by":[56,86],"12":[57],"layers":[58],"and":[59,76,126],"second":[61],"one":[62],"has":[63,139],"10":[64,111],"layers.":[65],"have":[69],"been":[70],"trained":[71],"separately,":[72],"different":[74,77],"architectures":[75],"sets":[78],"parameters.":[80],"final":[82],"decision":[83],"influenced":[85],"confidence":[88],"degrees":[89],"CNN":[92],"component":[93],"modules.":[94],"evaluated":[99],"dataset":[102,117],"400":[104],"images":[108,121,129],"belonging":[109],"subjects.":[112],"For":[113],"each":[114],"contains":[118],"20":[119,128],"corresponding":[122],"sober":[124],"condition":[125,132],"other":[127],"inebriation":[131],"obtained":[133],"30":[134],"minutes":[135],"after":[136],"drunk":[140],"100":[141],"ml":[142],"amount":[143],"whisky.":[145],"experiments":[147],"DCNN":[151],"couple":[152],"lead":[158],"overall":[161],"correct":[162],"score":[164],"95.75%.":[166],"confirms":[168],"effectiveness":[170],"approach.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}