{"id":"https://openalex.org/W2942771086","doi":"https://doi.org/10.1109/ictas.2019.8703521","title":"A Review of Local, Holistic and Deep Learning Approaches in Facial Expressions Recognition","display_name":"A Review of Local, Holistic and Deep Learning Approaches in Facial Expressions Recognition","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2942771086","doi":"https://doi.org/10.1109/ictas.2019.8703521","mag":"2942771086"},"language":"en","primary_location":{"id":"doi:10.1109/ictas.2019.8703521","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictas.2019.8703521","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Conference on Information Communications Technology and Society (ICTAS)","raw_type":"proceedings-article"},"type":"review","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/A5040730797","display_name":"Kennedy Chengeta","orcid":null},"institutions":[{"id":"https://openalex.org/I95023434","display_name":"University of KwaZulu-Natal","ror":"https://ror.org/04qzfn040","country_code":"ZA","type":"education","lineage":["https://openalex.org/I95023434"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Kennedy Chengeta","raw_affiliation_strings":["School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa","institution_ids":["https://openalex.org/I95023434"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054315649","display_name":"Serestina Viriri","orcid":"https://orcid.org/0000-0002-2850-8645"},"institutions":[{"id":"https://openalex.org/I95023434","display_name":"University of KwaZulu-Natal","ror":"https://ror.org/04qzfn040","country_code":"ZA","type":"education","lineage":["https://openalex.org/I95023434"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Serestina Viriri","raw_affiliation_strings":["School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa","institution_ids":["https://openalex.org/I95023434"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5040730797"],"corresponding_institution_ids":["https://openalex.org/I95023434"],"apc_list":null,"apc_paid":null,"fwci":1.2147,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.83070042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9988999962806702,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.992900013923645,"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/T11448","display_name":"Face recognition and analysis","score":0.9577999711036682,"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":0.8199647068977356},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7305139303207397},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.728937566280365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7275009751319885},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7253454923629761},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5860340595245361},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5694577693939209},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.5635714530944824},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4974105656147003},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4848848283290863},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4563257694244385},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.44708824157714844},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4253690838813782},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4110201597213745},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3433516323566437},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1913875937461853}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8199647068977356},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7305139303207397},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.728937566280365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7275009751319885},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7253454923629761},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5860340595245361},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5694577693939209},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.5635714530944824},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4974105656147003},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4848848283290863},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4563257694244385},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.44708824157714844},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4253690838813782},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4110201597213745},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3433516323566437},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1913875937461853},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictas.2019.8703521","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictas.2019.8703521","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Conference on Information Communications Technology and Society (ICTAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6800000071525574,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W208128215","https://openalex.org/W259338706","https://openalex.org/W1489504112","https://openalex.org/W1516748092","https://openalex.org/W1530517206","https://openalex.org/W1964762821","https://openalex.org/W1996459352","https://openalex.org/W2014835756","https://openalex.org/W2037651268","https://openalex.org/W2041682503","https://openalex.org/W2072032995","https://openalex.org/W2095353336","https://openalex.org/W2100041876","https://openalex.org/W2103943262","https://openalex.org/W2116836390","https://openalex.org/W2131119629","https://openalex.org/W2137409775","https://openalex.org/W2140190241","https://openalex.org/W2141415666","https://openalex.org/W2161969291","https://openalex.org/W2516455388","https://openalex.org/W2536032018","https://openalex.org/W2561332524","https://openalex.org/W2770769349","https://openalex.org/W2783119741","https://openalex.org/W2805568747","https://openalex.org/W3012325339","https://openalex.org/W3097096317","https://openalex.org/W4225831823","https://openalex.org/W4239356691","https://openalex.org/W4252791518","https://openalex.org/W4254852674","https://openalex.org/W4293768955","https://openalex.org/W6677220294","https://openalex.org/W6998697670"],"related_works":["https://openalex.org/W2172196609","https://openalex.org/W1995039490","https://openalex.org/W2116423617","https://openalex.org/W2182646186","https://openalex.org/W2003054897","https://openalex.org/W2077878713","https://openalex.org/W1999808563","https://openalex.org/W2046724945","https://openalex.org/W2370292837","https://openalex.org/W4224879220"],"abstract_inverted_index":{"In":[0],"facial":[1,53,82,147,214],"expression":[2,148,215],"identification,":[3],"algorithms":[4,37,49,57,107,191,198,228],"with":[5,195,217],"higher":[6],"classification":[7,21],"rates":[8],"and":[9,20,25,44,55,70,100,133,140,153,159,221],"lower":[10],"computational":[11,138],"costs":[12],"are":[13,130,162,168,229],"preferred.":[14],"To":[15],"achieve":[16],"that,":[17],"feature":[18,34,77,102,105,114],"extraction":[19,28,106],"should":[22],"be":[23],"accurate":[24],"efficient.":[26],"Feature":[27],"optimization":[29],"involves":[30,118],"selecting":[31],"the":[32,51,143,174],"optimal":[33],"descriptor.":[35],"Various":[36],"in":[38,112,199],"computer":[39],"vision":[40],"involve":[41,79],"holistic,":[42,151],"local":[43,81,96,113,152,190],"deep":[45,154,196],"learning":[46,117,155,197],"algorithms.":[47,156],"Holistic":[48],"analyze":[50],"whole":[52],"image":[54,124,184],"includes":[56],"like":[58],"Linear":[59],"Discriminant":[60],"Analysis":[61],"or":[62],"fisherfaces,":[63],"eigenfaces":[64],"(PCA),":[65],"Histograms":[66],"of":[67,142,146,173,201],"Oriented":[68],"Gradients":[69],"Gray":[71],"Level":[72],"Co-occurrence":[73],"Matrix":[74],"(GLCM).":[75],"Local":[76,92],"descriptors":[78],"using":[80,119],"components":[83],"separately":[84],"then":[85],"aggregating":[86],"them":[87],"into":[88],"a":[89],"combined":[90],"histogram.":[91],"binary":[93],"patterns":[94,98],"(LBP),":[95],"directional":[97],"(LDP)":[99],"scale-invariant":[101],"transform":[103],"(SIFT)":[104],"have":[108],"been":[109],"successfully":[110],"used":[111,163,169,182],"extraction.":[115],"Deep":[116],"convolutional":[120,209],"neural":[121],"networks":[122],"for":[123,164,170,183,223],"analysis.":[125,165],"The":[126,135,157,186],"most":[127],"popular":[128],"models":[129],"AlexNet,":[131],"VGG-Face":[132],"GoogleNet.":[134],"study":[136,187],"evaluates":[137],"accuracy":[139,202],"efficiency":[141],"three":[144],"forms":[145],"recognition":[149],"namely":[150],"JAFFE":[158],"CK+":[160],"datasets":[161],"Gabor":[166],"Filters":[167],"preprocessing":[171],"filtering":[172],"images":[175],"whilst":[176],"Viola":[177],"Jones":[178],"OpenCV":[179],"toolset":[180],"is":[181],"visualization.":[185],"concludes":[188],"that":[189],"compete":[192],"very":[193],"well":[194],"terms":[200],"but":[203],"use":[204],"less":[205],"processing":[206,219],"power":[207,220],"than":[208],"networks.":[210],"For":[211],"real":[212],"time":[213],"analysis":[216],"minimal":[218],"need":[222],"quick":[224],"response":[225],"times,":[226],"LBP":[227],"recommended.":[230]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}