{"id":"https://openalex.org/W2912204580","doi":"https://doi.org/10.48550/arxiv.1902.05411","title":"Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images","display_name":"Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images","publication_year":2019,"publication_date":"2019-02-12","ids":{"openalex":"https://openalex.org/W2912204580","doi":"https://doi.org/10.48550/arxiv.1902.05411","mag":"2912204580"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1902.05411","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.05411","pdf_url":"https://arxiv.org/pdf/1902.05411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1902.05411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Pandey, Ram Krishna","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pandey, Ram Krishna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071208816","display_name":"Souvik Karmakar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karmakar, Souvik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111880723","display_name":"A. G. Ramakrishnan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramakrishnan, A G","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046804142","display_name":"Nabagata Saha","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saha, Nabagata","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9926999807357788,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9926999807357788,"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/T11448","display_name":"Face recognition and analysis","score":0.005200000014156103,"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/T10057","display_name":"Face and Expression Recognition","score":0.0013000000035390258,"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/facial-emotion-recognition","display_name":"facial emotion recognition","score":0.9990000128746033},{"id":"https://openalex.org/keywords/gradient-image","display_name":"gradient image","score":0.824999988079071},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8939999938011169},{"id":"https://openalex.org/keywords/fiducial-points","display_name":"fiducial points","score":0.8119999766349792},{"id":"https://openalex.org/keywords/feature-enhancement","display_name":"feature enhancement","score":0.5019999742507935},{"id":"https://openalex.org/keywords/kdef-dataset","display_name":"KDEF dataset","score":0.843999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7339591979980469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7260051965713501},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.696035623550415},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6408320665359497},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6322063207626343},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6280127167701721},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.521337628364563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5198929309844971},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.46630990505218506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16620993614196777},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1902.05411","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.05411","pdf_url":"https://arxiv.org/pdf/1902.05411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1902.05411","doi":"https://doi.org/10.48550/arxiv.1902.05411","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1902.05411","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1902.05411","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.05411","pdf_url":"https://arxiv.org/pdf/1902.05411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2912204580.pdf","grobid_xml":"https://content.openalex.org/works/W2912204580.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W253816493","https://openalex.org/W603908379","https://openalex.org/W1564419782","https://openalex.org/W1686810756","https://openalex.org/W1854318472","https://openalex.org/W1996958556","https://openalex.org/W2009375902","https://openalex.org/W2024605627","https://openalex.org/W2037976268","https://openalex.org/W2074788634","https://openalex.org/W2094011994","https://openalex.org/W2103943262","https://openalex.org/W2116360511","https://openalex.org/W2128872156","https://openalex.org/W2139916508","https://openalex.org/W2145310492","https://openalex.org/W2154461202","https://openalex.org/W2167575933","https://openalex.org/W2217426128","https://openalex.org/W2251198138","https://openalex.org/W2298865080","https://openalex.org/W2481681431","https://openalex.org/W2611154248","https://openalex.org/W2767103364","https://openalex.org/W2963163009","https://openalex.org/W2964121744","https://openalex.org/W2982986041","https://openalex.org/W2995034616"],"related_works":["https://openalex.org/W2971532285","https://openalex.org/W4412965095","https://openalex.org/W4240500943","https://openalex.org/W3022246191","https://openalex.org/W4381512212","https://openalex.org/W2629163415","https://openalex.org/W4392981765","https://openalex.org/W4414348892","https://openalex.org/W4391857691","https://openalex.org/W4408275599"],"abstract_inverted_index":{"In":[0],"this":[1,95],"work,":[2],"we":[3],"have":[4,102],"proposed":[5],"several":[6],"enhancements":[7],"to":[8,93,129],"improve":[9],"the":[10,22,25,28,32,35,39,47,50,53,56,61,73,79,84,87,98,119],"performance":[11,122],"of":[12,27,41,49,55,83,106,123],"any":[13],"facial":[14],"emotion":[15,40],"recognition":[16],"(FER)":[17],"system.":[18],"We":[19,45,101],"believe":[20],"that":[21],"changes":[23],"in":[24],"positions":[26],"fiducial":[29],"points":[30],"and":[31,52,81,114],"intensities":[33],"capture":[34],"crucial":[36],"information":[37,77,96],"regarding":[38],"a":[42,65,104],"face":[43],"image.":[44],"propose":[46],"use":[48],"gradient":[51,80],"Laplacian":[54,82],"input":[57,63],"image":[58],"together":[59],"with":[60],"original":[62],"into":[64],"convolutional":[66],"neural":[67],"network":[68,74],"(CNN).":[69],"These":[70],"modifications":[71],"help":[72],"learn":[75],"additional":[76],"from":[78,97],"images.":[85,100],"However,":[86],"plain":[88],"CNN":[89],"is":[90],"not":[91],"able":[92],"extract":[94],"raw":[99],"performed":[103],"number":[105],"experiments":[107],"on":[108],"two":[109],"well":[110],"known":[111],"datasets":[112],"KDEF":[113],"FERplus.":[115],"Our":[116],"approach":[117],"enhances":[118],"already":[120],"high":[121],"state-of-the-art":[124],"FER":[125],"systems":[126],"by":[127],"3":[128],"5%.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-02-21T00:00:00"}
