{"id":"https://openalex.org/W4312431120","doi":"https://doi.org/10.1109/icpr56361.2022.9956537","title":"Neural Architecture Searching for Facial Attributes-based Depression Recognition","display_name":"Neural Architecture Searching for Facial Attributes-based Depression Recognition","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312431120","doi":"https://doi.org/10.1109/icpr56361.2022.9956537"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956537","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5101892811","display_name":"Mingzhe Chen","orcid":"https://orcid.org/0000-0002-2074-9487"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"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":"Mingzhe Chen","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047737162","display_name":"Xi Xiao","orcid":"https://orcid.org/0000-0002-6921-0669"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]},{"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":"Xi Xiao","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","Key Laboratory of Network Assessment Technology, Institute of Information Engineering, CAS, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Key Laboratory of Network Assessment Technology, Institute of Information Engineering, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392906","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0003-1303-2397"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhang","raw_affiliation_strings":["Peng Cheng Laboratory,Department of New Networks,Shenzhen,China","Department of New Networks, Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory,Department of New Networks,Shenzhen,China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Department of New Networks, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446419","display_name":"Xinyu Liu","orcid":"https://orcid.org/0000-0001-7857-5902"},"institutions":[{"id":"https://openalex.org/I4210159500","display_name":"Shenzhen Academy of Aerospace Technology","ror":"https://ror.org/05etzsm06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210159500"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Liu","raw_affiliation_strings":["Shenzhen Jinzhou Precision Technology Corp.,Shenzhen,China","Shenzhen Jinzhou Precision Technology Corp., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Jinzhou Precision Technology Corp.,Shenzhen,China","institution_ids":["https://openalex.org/I4210159500"]},{"raw_affiliation_string":"Shenzhen Jinzhou Precision Technology Corp., Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052969221","display_name":"Runiu Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"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":"Runiu Lu","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8275,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75098039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"877","last_page":"884"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9980999827384949,"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/T13283","display_name":"Mental Health Research Topics","score":0.9925000071525574,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7814956307411194},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5824288725852966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5583714246749878},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5125608444213867},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5093377828598022},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4692274034023285},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4518919289112091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4437779188156128},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4312693178653717},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34745410084724426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7814956307411194},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5824288725852966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5583714246749878},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5125608444213867},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5093377828598022},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4692274034023285},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4518919289112091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4437779188156128},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4312693178653717},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34745410084724426},{"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/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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956537","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"},{"id":"https://openalex.org/F4320337986","display_name":"Tsinghua Shenzhen International Graduate School","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1488266690","https://openalex.org/W1968600824","https://openalex.org/W2060557225","https://openalex.org/W2065003420","https://openalex.org/W2163928333","https://openalex.org/W2252180568","https://openalex.org/W2395639500","https://openalex.org/W2529925562","https://openalex.org/W2530305026","https://openalex.org/W2531271152","https://openalex.org/W2553303224","https://openalex.org/W2558919063","https://openalex.org/W2736601468","https://openalex.org/W2747469359","https://openalex.org/W2766415998","https://openalex.org/W2767030624","https://openalex.org/W2800833042","https://openalex.org/W2803017656","https://openalex.org/W2891155597","https://openalex.org/W2900794986","https://openalex.org/W2901769626","https://openalex.org/W2915992092","https://openalex.org/W2949299266","https://openalex.org/W2951104886","https://openalex.org/W2953604046","https://openalex.org/W2960672642","https://openalex.org/W2963918968","https://openalex.org/W2965658867","https://openalex.org/W2981501230","https://openalex.org/W2981677410","https://openalex.org/W2994802880","https://openalex.org/W3003850414","https://openalex.org/W3015892324","https://openalex.org/W3034535818","https://openalex.org/W3034764953","https://openalex.org/W3094644839","https://openalex.org/W3098431772","https://openalex.org/W3155665398","https://openalex.org/W4255158661","https://openalex.org/W4295185264","https://openalex.org/W4297782938","https://openalex.org/W4300687381","https://openalex.org/W6691669583","https://openalex.org/W6729956949","https://openalex.org/W6729972426","https://openalex.org/W6741002519","https://openalex.org/W6742933596","https://openalex.org/W6748057086","https://openalex.org/W6752515464","https://openalex.org/W6755964169","https://openalex.org/W6756500184","https://openalex.org/W6760018943","https://openalex.org/W6763572160","https://openalex.org/W6773134570","https://openalex.org/W6775880165","https://openalex.org/W6989526189"],"related_works":["https://openalex.org/W1495042958","https://openalex.org/W2494338568","https://openalex.org/W2122678784","https://openalex.org/W2282510344","https://openalex.org/W139987158","https://openalex.org/W2183994405","https://openalex.org/W4234013965","https://openalex.org/W2349023125","https://openalex.org/W4253006236","https://openalex.org/W1976807322"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"show":[2,161],"that":[3,162],"depression":[4,70,145],"can":[5,73,114],"be":[6,74,115,148],"partially":[7],"reflected":[8],"from":[9,37],"human":[10],"facial":[11,14,68,97],"attributes.":[12],"Since":[13],"attributes":[15],"have":[16],"various":[17],"data":[18],"structures":[19],"and":[20,76,106,138,174,177,194],"carry":[21],"different":[22],"information,":[23],"existing":[24,182],"approaches":[25],"fail":[26],"to":[27,33,54,61,91,100,147,201],"specifically":[28],"consider":[29],"the":[30,45,92,103,139,143,163,181],"optimal":[31,64],"way":[32],"extract":[34],"depression-related":[35],"features":[36],"each":[38,96,111],"of":[39,95,186],"them,":[40],"as":[41,43],"well":[42],"investigate":[44],"best":[46],"fusion":[47,140],"strategy.":[48],"In":[49,184],"this":[50,189],"paper,":[51],"we":[52,128],"propose":[53],"extend":[55],"Neural":[56,119,124],"Architecture":[57],"Search":[58],"(NAS)":[59],"technique":[60],"design":[62],"an":[63,130],"model":[65,164],"for":[66,134,198],"multiple":[67],"attributes-based":[69],"recognition,":[71],"which":[72],"efficiently":[75],"robustly":[77],"implemented":[78],"in":[79],"a":[80,87,117,195],"small":[81],"dataset.":[82],"Our":[83],"approach":[84,168],"first":[85],"conducts":[86],"warmer":[88],"up":[89],"step":[90],"feature":[93,112,136],"extractor":[94,113],"attribute,":[98],"aiming":[99],"largely":[101],"reduce":[102],"search":[104,133],"space":[105],"provide":[107],"customized":[108],"architecture,":[109],"where":[110],"either":[116],"Convolution":[118],"Networks":[120,125],"(CNN)":[121],"or":[122],"Graph":[123],"(GNN).":[126],"Then,":[127],"conduct":[129],"end-to-end":[131],"architecture":[132],"all":[135],"extractors":[137],"network,":[141],"allowing":[142],"complementary":[144],"cues":[146],"optimally":[149],"combined":[150],"with":[151,172],"less":[152],"redundancy.":[153],"The":[154],"experimental":[155],"results":[156],"on":[157],"AVEC":[158],"2016":[159],"dataset":[160],"explored":[165],"by":[166],"our":[167],"achieves":[169],"breakthrough":[170],"performance":[171],"27%":[173],"30%":[175],"RMSE":[176],"MAE":[178],"improvements":[179],"over":[180],"state-of-the-art.":[183],"light":[185],"these":[187],"findings,":[188],"paper":[190],"provides":[191],"solid":[192],"evidence":[193],"strong":[196],"baseline":[197],"applying":[199],"NAS":[200],"time-series":[202],"data-based":[203],"mental":[204],"health":[205],"analysis.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}