{"id":"https://openalex.org/W2546815862","doi":"https://doi.org/10.1145/2993148.2997628","title":"A deep look into group happiness prediction from images","display_name":"A deep look into group happiness prediction from images","publication_year":2016,"publication_date":"2016-10-31","ids":{"openalex":"https://openalex.org/W2546815862","doi":"https://doi.org/10.1145/2993148.2997628","mag":"2546815862"},"language":"en","primary_location":{"id":"doi:10.1145/2993148.2997628","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2993148.2997628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"article","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/A5065828621","display_name":"Aleksandra \u010cerekovi\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118256","display_name":"N&N Pharmaceuticals (United States)","ror":"https://ror.org/02m2ek616","country_code":"US","type":"company","lineage":["https://openalex.org/I4210118256"]},{"id":"https://openalex.org/I4210121272","display_name":"Anna Needs Neuroblastoma Answers","ror":"https://ror.org/02z5k8341","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210121272"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aleksandra Cerekovic","raw_affiliation_strings":["n.n., n.n"],"affiliations":[{"raw_affiliation_string":"n.n., n.n","institution_ids":["https://openalex.org/I4210118256","https://openalex.org/I4210121272"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5065828621"],"corresponding_institution_ids":["https://openalex.org/I4210118256","https://openalex.org/I4210121272"],"apc_list":null,"apc_paid":null,"fwci":1.169,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85531836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"437","last_page":"444"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9900000095367432,"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/T11448","display_name":"Face recognition and analysis","score":0.9900000095367432,"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":0.9638000130653381,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/happiness","display_name":"Happiness","score":0.9121432304382324},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7202938795089722},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6840134263038635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6673098802566528},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5949130654335022},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5623806715011597},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5602585077285767},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5495558381080627},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5319277048110962},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.5083014369010925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5024449825286865},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.47400200366973877},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4437134265899658},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4276203215122223},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.0986456573009491}],"concepts":[{"id":"https://openalex.org/C2778999518","wikidata":"https://www.wikidata.org/wiki/Q8","display_name":"Happiness","level":2,"score":0.9121432304382324},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7202938795089722},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6840134263038635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6673098802566528},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5949130654335022},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5623806715011597},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5602585077285767},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5495558381080627},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5319277048110962},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.5083014369010925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5024449825286865},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.47400200366973877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4437134265899658},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4276203215122223},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0986456573009491},{"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/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2993148.2997628","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2993148.2997628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W133839609","https://openalex.org/W1522301498","https://openalex.org/W1603712378","https://openalex.org/W1607979445","https://openalex.org/W1686810756","https://openalex.org/W2064675550","https://openalex.org/W2094637479","https://openalex.org/W2095705004","https://openalex.org/W2102113734","https://openalex.org/W2113855951","https://openalex.org/W2135964285","https://openalex.org/W2148718796","https://openalex.org/W2154683974","https://openalex.org/W2161381512","https://openalex.org/W2294427751","https://openalex.org/W2546649374","https://openalex.org/W2950179405","https://openalex.org/W2951132567","https://openalex.org/W2951912364","https://openalex.org/W2952453038"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W2347830809","https://openalex.org/W3027220834","https://openalex.org/W2377373329","https://openalex.org/W2032086542","https://openalex.org/W3207760230","https://openalex.org/W4386390828","https://openalex.org/W162406202","https://openalex.org/W2349081792","https://openalex.org/W1496222301"],"abstract_inverted_index":{"We":[0,73],"propose":[1],"a":[2,13,56,96],"Deep":[3],"Neural":[4],"Network":[5],"(DNN)":[6],"architecture":[7,94],"to":[8,66,100],"predict":[9,101],"happiness":[10,47,71,86,103],"displayed":[11],"by":[12,38],"group":[14,70,85,102],"of":[15,53,114],"people":[16],"in":[17],"images.":[18],"Our":[19,88,108],"approach":[20,77],"exploits":[21],"both":[22],"image":[23,63],"context":[24,64],"and":[25,49],"individual":[26],"facial":[27],"information":[28],"extracted":[29],"from":[30,105],"an":[31,106,112],"image.":[32,107],"The":[33],"latter":[34],"is":[35],"explicitly":[36],"modeled":[37],"Long":[39],"Short":[40],"Term":[41],"Memory":[42],"networks":[43],"(LSTMs)":[44],"encoding":[45],"face":[46],"intensity":[48],"the":[50,68,79,92,118,123],"spatial":[51],"distribution":[52],"faces":[54],"forming":[55],"group.":[57],"LSTM":[58],"outputs":[59],"are":[60],"combined":[61],"with":[62],"descriptors,":[65],"obtain":[67],"final":[69],"score.":[72],"thoroughly":[74],"evaluate":[75],"our":[76],"on":[78,122],"recently":[80],"proposed":[81,93],"HAPPEI":[82,119],"dataset":[83],"for":[84],"prediction.":[87],"results":[89],"show":[90],"that":[91],"outperforms":[95],"baseline":[97,121],"CNN":[98],"trained":[99],"directly":[104],"method":[109],"also":[110],"shows":[111],"improvement":[113],"approximately":[115],"30%":[116],"over":[117],"dataset's":[120],"validation":[124],"set.":[125]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3}],"updated_date":"2026-02-24T19:35:01.260952","created_date":"2025-10-10T00:00:00"}