{"id":"https://openalex.org/W2247689392","doi":"https://doi.org/10.1109/icnc.2015.7378076","title":"Facial expression recognition based on transfer learning from deep convolutional networks","display_name":"Facial expression recognition based on transfer learning from deep convolutional networks","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2247689392","doi":"https://doi.org/10.1109/icnc.2015.7378076","mag":"2247689392"},"language":"en","primary_location":{"id":"doi:10.1109/icnc.2015.7378076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2015.7378076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 11th International Conference on Natural Computation (ICNC)","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/A5112524017","display_name":"Mao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mao Xu","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103166680","display_name":"Wei Cheng","orcid":"https://orcid.org/0000-0003-3703-9622"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086649532","display_name":"Qian Zhao","orcid":"https://orcid.org/0000-0002-3758-237X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Zhao","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059216139","display_name":"Li Ma","orcid":"https://orcid.org/0000-0001-5158-2264"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Ma","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100684651","display_name":"Fang Xu","orcid":"https://orcid.org/0000-0003-1458-9875"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Xu","raw_affiliation_strings":["School of Science, Southwest Petroleum University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":72,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"702","last_page":"708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","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/T10667","display_name":"Emotion and Mood Recognition","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/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9962000250816345,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7811161279678345},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7533667087554932},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.74648118019104},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6642385721206665},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6376197934150696},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6357535123825073},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6353442072868347},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6276363134384155},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5845646262168884},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.5681018233299255},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4766610264778137},{"id":"https://openalex.org/keywords/facial-expression-recognition","display_name":"Facial expression recognition","score":0.4639856815338135},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3828352093696594}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7811161279678345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533667087554932},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.74648118019104},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6642385721206665},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6376197934150696},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6357535123825073},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6353442072868347},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6276363134384155},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5845646262168884},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5681018233299255},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4766610264778137},{"id":"https://openalex.org/C2987714656","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Facial expression recognition","level":4,"score":0.4639856815338135},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3828352093696594}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc.2015.7378076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2015.7378076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 11th International Conference on Natural Computation (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W175058090","https://openalex.org/W1579590535","https://openalex.org/W1896424170","https://openalex.org/W1988744266","https://openalex.org/W1998808035","https://openalex.org/W2009119155","https://openalex.org/W2024605627","https://openalex.org/W2046677541","https://openalex.org/W2053432263","https://openalex.org/W2058098602","https://openalex.org/W2096044434","https://openalex.org/W2103384342","https://openalex.org/W2103943262","https://openalex.org/W2121114545","https://openalex.org/W2126565853","https://openalex.org/W2128958079","https://openalex.org/W2136063698","https://openalex.org/W2146566773","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2157285372","https://openalex.org/W2160947254","https://openalex.org/W2163605009","https://openalex.org/W2282078507","https://openalex.org/W2995034616","https://openalex.org/W3097096317","https://openalex.org/W4285719527","https://openalex.org/W6639495226","https://openalex.org/W6661938819","https://openalex.org/W6679060099","https://openalex.org/W6684191040"],"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":{"It":[0],"is":[1],"well-known":[2],"that":[3],"deep":[4,25,32,52,190],"models":[5],"could":[6,157],"extract":[7],"robust":[8],"and":[9,45,59,81,111,153,196,214],"abstract":[10],"features.":[11],"We":[12,29],"propose":[13],"a":[14,67,72],"efficient":[15],"facial":[16,62,73,88,137,176,222],"expression":[17,63,74,89,138,177,223],"recognition":[18,64,101,113,127,178],"model":[19,53,65,148,156,182,203],"based":[20,103,115],"on":[21,41,66,86,104,116,134,219],"transfer":[22,46],"features":[23,48,106,118,185],"from":[24,49,187],"convolutional":[26],"networks":[27],"(ConvNets).":[28],"train":[30,58],"the":[31,35,42,50,61,108,120,130,135,150,155,164,170,175,193,197,211,220],"ConvNets":[33,191],"through":[34],"task":[36],"of":[37,76,161,192,208],"1580-class":[38],"face":[39,142],"identification":[40],"MSRA-CFW":[43],"database":[44,75],"high-level":[47,184],"trained":[51,189],"to":[54],"recognize":[55],"expression.":[56],"To":[57,168],"test":[60,146],"large":[68],"scope,":[69],"we":[70,123,145,173],"built":[71],"seven":[77],"basic":[78],"emotion":[79],"states":[80],"2062":[82],"imbalanced":[83],"samples":[84],"depending":[85],"four":[87],"databases":[90],"(CK+,":[91],"JAFFE,":[92],"KDEF,":[93],"Pain":[94],"expressions":[95],"form":[96],"PICS).":[97],"Compared":[98],"with":[99,107,119,129],"50.65%":[100],"rate":[102,114,128],"Gabor":[105],"seven-class":[109,121,131],"SVM":[110,132],"78.84%":[112],"distance":[117],"SVM,":[122],"achieve":[124],"average":[125,216],"80.49%":[126],"classifier":[133],"self-built":[136,221],"database.":[139,224],"Considering":[140],"occluded":[141,151,212],"in":[143,149,163,210],"reality,":[144],"our":[147],"condition":[152,213],"demonstrate":[154],"keep":[158],"its":[159,206],"ability":[160,171,207],"classification":[162,209],"small":[165],"occlusion":[166],"case.":[167],"increase":[169],"further,":[172],"improve":[174],"model.":[179],"The":[180,201],"modified":[181,202],"merges":[183],"transferred":[186],"two":[188],"same":[194],"structure":[195],"different":[198],"training":[199],"sets.":[200],"obviously":[204],"improves":[205],"achieves":[215],"81.50%":[217],"accuracy":[218]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}