{"id":"https://openalex.org/W4324143524","doi":"https://doi.org/10.1145/3582700.3583705","title":"Exploring the Effect of Transfer Learning on Facial Expression Recognition using Photo-Reflective Sensors embedded into a Head-Mounted Display","display_name":"Exploring the Effect of Transfer Learning on Facial Expression Recognition using Photo-Reflective Sensors embedded into a Head-Mounted Display","publication_year":2023,"publication_date":"2023-03-12","ids":{"openalex":"https://openalex.org/W4324143524","doi":"https://doi.org/10.1145/3582700.3583705"},"language":"en","primary_location":{"id":"doi:10.1145/3582700.3583705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582700.3583705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Augmented Humans Conference","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/A5007200758","display_name":"Fumihiko Nakamura","orcid":"https://orcid.org/0000-0001-6285-3963"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fumihiko Nakamura","raw_affiliation_strings":["Keio University, Japan"],"raw_orcid":"https://orcid.org/0000-0001-6285-3963","affiliations":[{"raw_affiliation_string":"Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087706411","display_name":"Maki Sugimoto","orcid":"https://orcid.org/0000-0002-8383-9228"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Maki Sugimoto","raw_affiliation_strings":["Keio University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-8383-9228","affiliations":[{"raw_affiliation_string":"Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"317","last_page":"319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9751999974250793,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9751999974250793,"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/T10057","display_name":"Face and Expression Recognition","score":0.9146000146865845,"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/classifier","display_name":"Classifier (UML)","score":0.7962796688079834},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.7687472701072693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.756702721118927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575304269790649},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5882110595703125},{"id":"https://openalex.org/keywords/facial-expression-recognition","display_name":"Facial expression recognition","score":0.5662424564361572},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47658559679985046},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4704992473125458},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.40577247738838196}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7962796688079834},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.7687472701072693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756702721118927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575304269790649},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5882110595703125},{"id":"https://openalex.org/C2987714656","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Facial expression recognition","level":4,"score":0.5662424564361572},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47658559679985046},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4704992473125458},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.40577247738838196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582700.3583705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582700.3583705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Augmented Humans Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1693062721","display_name":"HMD\u7d44\u307f\u8fbc\u307f\u578b\u5149\u30bb\u30f3\u30b5\u3092\u7528\u3044\u305f\u8868\u60c5\u8a8d\u8b58\u306b\u304a\u3051\u308b\u751f\u6210\u578b\u5b66\u7fd2","funder_award_id":"21J13664","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5295144349","display_name":"\u7a32\u898b\u81ea\u5728\u5316\u8eab\u4f53\u30d7\u30ed\u30b8\u30a7\u30af\u30c8","funder_award_id":"JPMJER1701","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2604635052","https://openalex.org/W2887015488","https://openalex.org/W2895613288","https://openalex.org/W2962737616","https://openalex.org/W3112225120","https://openalex.org/W4282927477"],"related_works":["https://openalex.org/W2979740303","https://openalex.org/W2355913164","https://openalex.org/W4205986151","https://openalex.org/W2168968280","https://openalex.org/W2116055069","https://openalex.org/W4323520705","https://openalex.org/W2356663679","https://openalex.org/W2169777806","https://openalex.org/W3027190010","https://openalex.org/W2162992774"],"abstract_inverted_index":{"As":[0],"one":[1],"of":[2,24,51,88],"the":[3,13,21,41,79,100,106,127,144,148,157],"techniques":[4],"to":[5,44,54,143],"recognize":[6],"head-mounted":[7],"display":[8],"(HMD)":[9],"user\u2019s":[10],"facial":[11,36,57,81,90,110,131],"expressions,":[12],"photo-reflective":[14],"sensor":[15],"(PRS)":[16],"has":[17],"been":[18],"employed.":[19],"Since":[20],"classification":[22,112,133,154],"performance":[23],"PRS-based":[25,80],"method":[26],"is":[27],"affected":[28],"by":[29],"rewearing":[30],"an":[31,52],"HMD":[32,53,102],"and":[33,71,74,129],"difference":[34],"in":[35,78],"geometry":[37],"for":[38,48],"each":[39,49],"user,":[40],"user":[42],"have":[43],"perform":[45],"dataset":[46,87,151],"collection":[47],"wearing":[50],"build":[55],"a":[56,86,115],"expression":[58,82,111,132],"classifier.":[59,138,159],"To":[60],"tackle":[61],"this":[62],"issue,":[63],"we":[64,108],"investigate":[65],"how":[66],"transfer":[67],"learning":[68],"improve":[69],"within-user":[70,128],"cross-user":[72,130],"accuracy":[73,113,134,155],"reduce":[75],"training":[76],"data":[77],"recognition.":[83],"We":[84],"collected":[85],"five":[89,103],"expressions":[91],"(Neutral,":[92],"Smile,":[93],"Angry,":[94],"Surprised,":[95],"Sad)":[96],"when":[97],"participants":[98],"wore":[99],"PRS-embedded":[101],"times.":[104],"Using":[105],"dataset,":[107],"evaluated":[109],"using":[114],"neural":[116],"network":[117],"with/without":[118],"fine":[119,124,141],"tuning.":[120],"Our":[121],"result":[122],"showed":[123],"tuning":[125,142],"improved":[126],"compared":[135],"with":[136,147],"non-fine-tuned":[137,158],"Also,":[139],"applying":[140],"classifier":[145],"trained":[146],"other":[149],"participant":[150],"achieved":[152],"higher":[153],"than":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
