{"id":"https://openalex.org/W2808224902","doi":"https://doi.org/10.1109/icip.2018.8451065","title":"CFD: A Collaborative Feature Difference Method for Spontaneous Micro-Expression Spotting","display_name":"CFD: A Collaborative Feature Difference Method for Spontaneous Micro-Expression Spotting","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2808224902","doi":"https://doi.org/10.1109/icip.2018.8451065","mag":"2808224902"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","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/A5035402576","display_name":"Yiheng Han","orcid":"https://orcid.org/0000-0002-3986-7555"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiheng Han","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101530082","display_name":"Bingjun Li","orcid":"https://orcid.org/0000-0003-0513-3497"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingjun Li","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067850699","display_name":"Yu\u2010Kun Lai","orcid":"https://orcid.org/0000-0002-2094-5680"},"institutions":[{"id":"https://openalex.org/I79510175","display_name":"Cardiff University","ror":"https://ror.org/03kk7td41","country_code":"GB","type":"education","lineage":["https://openalex.org/I79510175"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yu-Kun Lai","raw_affiliation_strings":["Cardiff University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cardiff University, UK","institution_ids":["https://openalex.org/I79510175"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008076279","display_name":"Yong\u2010Jin Liu","orcid":"https://orcid.org/0000-0001-5774-1916"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong-Jin Liu","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2651,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.80664188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1942","last_page":"1946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.993399977684021,"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":0.9927999973297119,"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/spotting","display_name":"Spotting","score":0.9536011815071106},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7264225482940674},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.643670380115509},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5902471542358398},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.5569015741348267},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.5519954562187195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5302391052246094},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5188180804252625},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44874119758605957}],"concepts":[{"id":"https://openalex.org/C2779506182","wikidata":"https://www.wikidata.org/wiki/Q7580141","display_name":"Spotting","level":2,"score":0.9536011815071106},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7264225482940674},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.643670380115509},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5902471542358398},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5569015741348267},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.5519954562187195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5302391052246094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5188180804252625},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44874119758605957},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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":2,"locations":[{"id":"doi:10.1109/icip.2018.8451065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:https://orca.cardiff.ac.uk:111703","is_oa":false,"landing_page_url":"https://orca.cardiff.ac.uk/id/eprint/111703/1/me-cfd-ICIP2018.pdf","pdf_url":"https://orca.cardiff.ac.uk/id/eprint/111703/1/me-cfd-ICIP2018.pdf","source":{"id":"https://openalex.org/S4306401195","display_name":"ORCA Online Research @Cardiff (Cardiff University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79510175","host_organization_name":"Cardiff University","host_organization_lineage":["https://openalex.org/I79510175"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W65124300","https://openalex.org/W65855405","https://openalex.org/W1854318472","https://openalex.org/W2006426145","https://openalex.org/W2039051707","https://openalex.org/W2044106642","https://openalex.org/W2056591378","https://openalex.org/W2072038603","https://openalex.org/W2101866605","https://openalex.org/W2139675209","https://openalex.org/W2163352848","https://openalex.org/W2166626612","https://openalex.org/W2196663747","https://openalex.org/W2526853616","https://openalex.org/W2538953432","https://openalex.org/W2567449792","https://openalex.org/W3103539074","https://openalex.org/W6786131635"],"related_works":["https://openalex.org/W2034439647","https://openalex.org/W4249589822","https://openalex.org/W2103063669","https://openalex.org/W3184921334","https://openalex.org/W4255446307","https://openalex.org/W2033009170","https://openalex.org/W2988098900","https://openalex.org/W4386895402","https://openalex.org/W2642127892","https://openalex.org/W2009284386"],"abstract_inverted_index":{"Micro-expression":[0],"(ME)":[1],"is":[2,24,46],"a":[3,32,42,79,98,113,118,130,153,161],"special":[4],"type":[5],"of":[6,38,100,106,112,211],"human":[7],"expression":[8],"which":[9],"can":[10],"reveal":[11],"the":[12,27,52,90,124,209,212],"real":[13],"emotion":[14],"that":[15,54,205],"people":[16],"want":[17],"to":[18,25,51,128,136,167],"conceal.":[19],"Spontaneous":[20],"ME":[21],"(SME)":[22],"spotting":[23,40,82],"identify":[26],"subsequences":[28],"containing":[29],"SMEs":[30,58],"from":[31],"long":[33],"facial":[34,64,101],"video.":[35],"The":[36],"study":[37],"SME":[39,81,198],"has":[41],"significant":[43],"importance,":[44],"but":[45],"also":[47],"very":[48],"challenging":[49],"due":[50],"fact":[53],"in":[55,89],"real-world":[56],"scenarios,":[57],"may":[59],"occur":[60],"along":[61],"with":[62,169],"normal":[63],"expressions":[65],"and":[66,109,148,159,178,201],"other":[67],"prominent":[68],"motions":[69],"such":[70],"as":[71],"head":[72],"movements.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77],"improve":[78],"state-of-the-art":[80,154],"method":[83,121,188],"called":[84],"feature":[85,156,190],"difference":[86,191],"analysis":[87],"(FD)":[88],"following":[91],"two":[92,145,170,196],"aspects.":[93],"First,":[94],"FD":[95,142,158,187],"relies":[96],"on":[97,195],"partitioning":[99],"area":[102],"into":[103,157],"uniform":[104],"regions":[105],"interest":[107],"(ROIs)":[108],"computing":[110],"features":[111,146],"selected":[114],"sequence.":[115],"We":[116,151,183],"propose":[117,160],"novel":[119],"evaluation":[120],"by":[122],"utilizing":[123],"Fisher":[125],"linear":[126],"discriminant":[127],"assign":[129],"weight":[131],"for":[132],"each":[133],"ROI,":[134],"leading":[135],"more":[137],"semantically":[138],"meaningful":[139],"ROIs.":[140],"Second,":[141],"only":[143],"considers":[144],"(LBP":[147],"HOOF)":[149],"independently.":[150],"introduce":[152],"MDMO":[155,179],"simple":[162],"yet":[163],"efficient":[164],"collaborative":[165,189],"strategy":[166],"work":[168],"complementary":[171],"features,":[172],"i.e.,":[173],"LBP":[174],"characterizing":[175,180],"texture":[176],"information":[177],"motion":[181],"information.":[182],"call":[184],"our":[185],"improved":[186],"(CFD).":[192],"Experimental":[193],"results":[194],"well-established":[197],"datasets":[199],"SMIC-E":[200],"CASME":[202],"II":[203],"show":[204],"CFD":[206],"significantly":[207],"improves":[208],"performance":[210],"original":[213],"FD.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}