{"id":"https://openalex.org/W4386597008","doi":"https://doi.org/10.1109/icip49359.2023.10221969","title":"Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection","display_name":"Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386597008","doi":"https://doi.org/10.1109/icip49359.2023.10221969"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10221969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10221969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5023679462","display_name":"Sota Kato","orcid":"https://orcid.org/0000-0003-0392-6426"},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Sota Kato","raw_affiliation_strings":["Meijo University,SenseTime,Japan","SenseTime, Meijo University, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University,SenseTime,Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"SenseTime, Meijo University, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103163418","display_name":"Kazuhiro Hotta","orcid":"https://orcid.org/0000-0002-5675-8713"},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiro Hotta","raw_affiliation_strings":["Meijo University,SenseTime,Japan","SenseTime, Meijo University, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University,SenseTime,Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"SenseTime, Meijo University, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012854752","display_name":"Yuhki Hatakeyama","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuhki Hatakeyama","raw_affiliation_strings":["Meijo University,SenseTime,Japan","SenseTime, Meijo University, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University,SenseTime,Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"SenseTime, Meijo University, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111747340","display_name":"Yoshinori Konishi","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshinori Konishi","raw_affiliation_strings":["Meijo University,SenseTime,Japan","SenseTime, Meijo University, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo University,SenseTime,Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"SenseTime, Meijo University, Japan","institution_ids":["https://openalex.org/I96636082"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023679462"],"corresponding_institution_ids":["https://openalex.org/I96636082"],"apc_list":null,"apc_paid":null,"fwci":0.8481,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75515194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1500","last_page":"1504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9995999932289124,"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":0.9995999932289124,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/landmark","display_name":"Landmark","score":0.9689556360244751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7666087746620178},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6864812970161438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6435045599937439},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.633460521697998},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6090694069862366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5350666046142578},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5229871273040771},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4418962001800537},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43908369541168213},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43232738971710205},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2708456516265869},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10548394918441772},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08077248930931091}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9689556360244751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7666087746620178},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6864812970161438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6435045599937439},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.633460521697998},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6090694069862366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5350666046142578},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5229871273040771},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4418962001800537},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43908369541168213},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43232738971710205},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2708456516265869},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10548394918441772},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08077248930931091},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10221969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10221969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1766486746","https://openalex.org/W1974071709","https://openalex.org/W2058961190","https://openalex.org/W2740020909","https://openalex.org/W2770121394","https://openalex.org/W2883780447","https://openalex.org/W2916798096","https://openalex.org/W2927589841","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963789946","https://openalex.org/W2982083293","https://openalex.org/W3010526057","https://openalex.org/W3014641072","https://openalex.org/W3171398643","https://openalex.org/W4285601475","https://openalex.org/W4297775537","https://openalex.org/W4392670026"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2026924879","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W2052388267","https://openalex.org/W2950647290","https://openalex.org/W1968481813","https://openalex.org/W2620829895","https://openalex.org/W2356918560"],"abstract_inverted_index":{"Facial":[0],"landmark":[1,21,124],"detection":[2],"is":[3,75],"an":[4],"essential":[5],"technology":[6],"for":[7,16],"driver":[8],"status":[9],"tracking":[10],"and":[11,33,105,140],"has":[12,61],"been":[13],"in":[14,73,135],"demand":[15],"real-time":[17],"estimations.":[18],"As":[19],"a":[20,30,37,46,85,96,102,106,142,146],"coordinate":[22],"prediction,":[23],"heatmap-based":[24],"methods":[25],"are":[26],"known":[27],"to":[28,63,78],"achieve":[29,36],"high":[31],"accuracy,":[32],"Lite-HRNet":[34,89,91,129],"can":[35],"fast":[38],"estimation.":[39],"However,":[40],"with":[41,58,110,137,145,149],"Lite-HRNet,":[42],"the":[43,51,68,133,150],"problem":[44],"of":[45,50,152],"heavy":[47],"computational":[48,112,147],"cost":[49],"fusion":[52,98],"block,":[53],"which":[54],"connects":[55],"feature":[56,116],"maps":[57],"different":[59],"resolutions,":[60],"yet":[62],"be":[64],"solved.":[65],"In":[66],"addition,":[67],"strong":[69],"output":[70,108],"module":[71,109],"used":[72],"HRNetV2":[74],"not":[76],"applied":[77],"Lite-HRNet.":[79],"Given":[80],"these":[81],"problems,":[82],"we":[83,126],"propose":[84],"novel":[86,97,107],"architecture":[87],"called":[88],"Plus.":[90],"Plus":[92,130],"achieves":[93],"two":[94,122],"improvements:":[95],"block":[99],"based":[100],"on":[101,121],"channel":[103],"attention":[104],"less":[111],"intensity":[113],"using":[114],"multi-resolution":[115],"maps.":[117],"Through":[118],"experiments":[119],"conducted":[120],"facial":[123],"datasets,":[125],"confirmed":[127],"that":[128],"further":[131],"improved":[132],"accuracy":[134,144],"comparison":[136],"conventional":[138],"methods,":[139],"achieved":[141],"state-of-the-art":[143],"complexity":[148],"range":[151],"10M":[153],"FLOPs.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}
