{"id":"https://openalex.org/W2970423132","doi":"https://doi.org/10.1109/icip.2019.8802934","title":"Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases","display_name":"Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970423132","doi":"https://doi.org/10.1109/icip.2019.8802934","mag":"2970423132"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8802934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8802934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5103095739","display_name":"Pengrui Wang","orcid":"https://orcid.org/0009-0007-9325-9693"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengrui Wang","raw_affiliation_strings":["Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102633812","display_name":"Yi Tian","orcid":"https://orcid.org/0009-0007-2429-740X"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Tian","raw_affiliation_strings":["Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030607500","display_name":"Wujun Che","orcid":"https://orcid.org/0000-0001-8230-0696"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wujun Che","raw_affiliation_strings":["Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108642431","display_name":"Bo Xu","orcid":"https://orcid.org/0000-0002-1111-1529"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Xu","raw_affiliation_strings":["Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103095739"],"corresponding_institution_ids":["https://openalex.org/I4210094879"],"apc_list":null,"apc_paid":null,"fwci":0.4049,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6521628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"335","last_page":"339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":1.0,"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":1.0,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11322","display_name":"Facial Rejuvenation and Surgery Techniques","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2708","display_name":"Dermatology"},"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/landmark","display_name":"Landmark","score":0.9425241947174072},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8247429132461548},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6954083442687988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6935163140296936},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5512233972549438},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.541408121585846},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5321261882781982},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5269142985343933},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5232211947441101},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45769932866096497},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0879291296005249}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9425241947174072},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8247429132461548},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6954083442687988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6935163140296936},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5512233972549438},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.541408121585846},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5321261882781982},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5269142985343933},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5232211947441101},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45769932866096497},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0879291296005249},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8802934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8802934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1782590233","https://openalex.org/W1935685005","https://openalex.org/W2012885984","https://openalex.org/W2013871799","https://openalex.org/W2017107803","https://openalex.org/W2058961190","https://openalex.org/W2062712751","https://openalex.org/W2265959009","https://openalex.org/W2301937176","https://openalex.org/W2398381847","https://openalex.org/W2486034530","https://openalex.org/W2555510177","https://openalex.org/W2593956217","https://openalex.org/W2604672468","https://openalex.org/W2612445135","https://openalex.org/W2771328060","https://openalex.org/W2795709097","https://openalex.org/W2806379360","https://openalex.org/W2810993953","https://openalex.org/W2889050557","https://openalex.org/W2890264026","https://openalex.org/W2949662773","https://openalex.org/W2952080583","https://openalex.org/W2962780596","https://openalex.org/W2963342110","https://openalex.org/W2963789946","https://openalex.org/W2964171387","https://openalex.org/W3104792420","https://openalex.org/W4297775537","https://openalex.org/W6638046521","https://openalex.org/W6640455264","https://openalex.org/W6654799313","https://openalex.org/W6712629191","https://openalex.org/W6735920323","https://openalex.org/W6737664043","https://openalex.org/W6746270531","https://openalex.org/W6754208138","https://openalex.org/W6754947180"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","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","https://openalex.org/W4243161226"],"abstract_inverted_index":{"We":[0,12],"propose":[1,60,90],"an":[2,14,113],"efficient":[3],"and":[4,25,82,100,140,150],"accurate":[5,99],"regression-based":[6],"3D":[7],"face":[8,23,30,35,103],"shape":[9,118],"reconstruction":[10,104],"method.":[11],"use":[13],"encoder":[15,38],"based":[16,94],"on":[17,136],"MobileNet":[18,93],"to":[19,65,120,144],"estimate":[20],"parameters":[21],"including":[22],"pose":[24],"coefficients":[26],"of":[27,54],"a":[28,33,61,91],"parametric":[29],"model":[31],"from":[32],"single":[34],"image.":[36],"The":[37],"is":[39,97],"trained":[40],"only":[41],"by":[42,49],"2D":[43,74],"landmarks.":[44],"Faces":[45],"can":[46,70,130],"be":[47],"reconstructed":[48],"these":[50],"parameters.":[51],"Three":[52],"contributions":[53],"our":[55,67,123,128],"method":[56,64,129],"are:":[57],"1)":[58],"we":[59,89,111],"databases":[62,76,139],"fusion":[63,87],"train":[66],"network":[68,95],"which":[69,77,96],"easily":[71],"utilize":[72],"multiple":[73],"landmark":[75,80],"have":[78],"different":[79],"numbers":[81],"positions;":[83],"2)":[84],"with":[85],"the":[86],"method,":[88],"simple":[92],"efficient,":[98],"robust":[101],"for":[102,117],"even":[105],"without":[106],"complex":[107],"training":[108],"strategies;":[109],"3)":[110],"add":[112],"additional":[114],"deformation":[115],"field":[116],"correction":[119],"further":[121],"improve":[122],"network's":[124],"performance.":[125],"Experiments":[126],"demonstrate":[127],"bring":[131],"about":[132],"great":[133],"performance":[134,149],"improvement":[135],"most":[137],"test":[138],"also":[141],"compare":[142],"favorably":[143],"some":[145],"state-of-the-art":[146],"methods":[147],"in":[148],"speed.":[151]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}