{"id":"https://openalex.org/W2751803194","doi":"https://doi.org/10.1109/icme.2017.8019454","title":"Facial attractiveness computation by label distribution learning with deep CNN and geometric features","display_name":"Facial attractiveness computation by label distribution learning with deep CNN and geometric features","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2751803194","doi":"https://doi.org/10.1109/icme.2017.8019454","mag":"2751803194"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2017.8019454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","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/A5064499660","display_name":"Shu Liu","orcid":"https://orcid.org/0000-0002-2903-9270"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Liu","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114375869","display_name":"Bo Li","orcid":"https://orcid.org/0009-0003-4088-1578"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015995416","display_name":"Yangyu Fan","orcid":"https://orcid.org/0000-0003-0689-5418"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang-Yu Fan","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079817402","display_name":"Zhe Quo","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Quo","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, Shaanxi, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, Shaanxi, CN","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056599026","display_name":"Ashok Samal","orcid":"https://orcid.org/0000-0002-4559-9454"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashok Samal","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Nebraska-Lincoln, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, USA","institution_ids":["https://openalex.org/I114395901"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1344","last_page":"1349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9902999997138977,"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":0.9902999997138977,"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/T11118","display_name":"Evolutionary Psychology and Human Behavior","score":0.9860000014305115,"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/T11812","display_name":"Nasal Surgery and Airway Studies","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8145405054092407},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7772022485733032},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7113457322120667},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7019427418708801},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6997025012969971},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5437094569206238},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5132324695587158},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5056600570678711},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46566131711006165},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4355451166629791},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2131720781326294},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10166880488395691}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8145405054092407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7772022485733032},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7113457322120667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019427418708801},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6997025012969971},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5437094569206238},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5132324695587158},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5056600570678711},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46566131711006165},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4355451166629791},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2131720781326294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10166880488395691},{"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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2017.8019454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W47403462","https://openalex.org/W619338307","https://openalex.org/W1686810756","https://openalex.org/W1883761234","https://openalex.org/W1972968514","https://openalex.org/W1993741516","https://openalex.org/W2031813546","https://openalex.org/W2066454034","https://openalex.org/W2096340072","https://openalex.org/W2108598243","https://openalex.org/W2112987325","https://openalex.org/W2146932984","https://openalex.org/W2151541811","https://openalex.org/W2152186880","https://openalex.org/W2155893237","https://openalex.org/W2157395013","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2169159124","https://openalex.org/W2194775991","https://openalex.org/W2229978099","https://openalex.org/W2231999017","https://openalex.org/W2401023183","https://openalex.org/W2518428477","https://openalex.org/W2543791852","https://openalex.org/W2962835968","https://openalex.org/W3156302520","https://openalex.org/W6637373629","https://openalex.org/W6676297131","https://openalex.org/W6682468274","https://openalex.org/W6682525936","https://openalex.org/W6684191040","https://openalex.org/W6689124258"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Facial":[0],"attractiveness":[1],"computation":[2],"is":[3,36],"a":[4],"challenging":[5],"task":[6,51],"because":[7],"of":[8,11,69,79,101],"the":[9,45,58,62,66,123,135],"lack":[10],"labeled":[12],"data":[13],"and":[14,33],"discriminative":[15,94],"features.":[16,103,116],"In":[17,73],"this":[18,50],"paper,":[19],"an":[20,53,85,98],"end-to-end":[21],"label":[22,60],"distribution":[23],"learning":[24],"(LDL)":[25],"framework":[26],"with":[27,57],"deep":[28],"convolutional":[29],"neural":[30],"network":[31],"(CNN)":[32],"geometric":[34,80,95,110],"features":[35,81,96,111],"proposed":[37],"to":[38,114,134],"meet":[39],"these":[40,108],"two":[41],"challenges.":[42],"Different":[43],"from":[44,97],"previous":[46],"work,":[47],"we":[48,75,106],"recast":[49],"as":[52,82,84],"LDL":[54,63],"problem.":[55],"Compared":[56],"single":[59],"regression,":[61],"could":[64,91],"improve":[65],"generalization":[67],"ability":[68],"our":[70,127],"model":[71],"significantly.":[72],"addition,":[74],"propose":[76],"some":[77],"kinds":[78],"well":[83],"incremental":[86],"feature":[87],"selection":[88],"method,":[89],"which":[90],"select":[92],"hundred-dimensional":[93],"exhaustive":[99],"pool":[100],"raw":[102],"More":[104],"importantly,":[105],"find":[107],"selected":[109],"are":[112,119],"complementary":[113],"CNN":[115],"Extensive":[117],"experiments":[118],"carried":[120],"out":[121],"on":[122],"SCUT-FBP":[124],"dataset,":[125],"where":[126],"approach":[128],"achieves":[129],"superior":[130],"performance":[131],"in":[132],"comparison":[133],"state-of-the-arts.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
