{"id":"https://openalex.org/W3063576435","doi":"https://doi.org/10.1109/iske47853.2019.9170374","title":"Residual Neural Network and Wing Loss for Face Alignment Network","display_name":"Residual Neural Network and Wing Loss for Face Alignment Network","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3063576435","doi":"https://doi.org/10.1109/iske47853.2019.9170374","mag":"3063576435"},"language":"en","primary_location":{"id":"doi:10.1109/iske47853.2019.9170374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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/A5070208872","display_name":"Li Wang","orcid":"https://orcid.org/0000-0002-8977-0137"},"institutions":[{"id":"https://openalex.org/I2799449666","display_name":"State Ethnic Affairs Commission","ror":"https://ror.org/01p9g6b97","country_code":"CN","type":"government","lineage":["https://openalex.org/I2799449666"]},{"id":"https://openalex.org/I6645349","display_name":"Southwest Minzu University","ror":"https://ror.org/04gaexw88","country_code":"CN","type":"education","lineage":["https://openalex.org/I6645349"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["(Southwest Minzu University) State Ethnic Affairs Commission,Key Laboratory of Electronic and Information Engineering,Chengdu,China","Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University) State Ethnic Affairs Commission, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(Southwest Minzu University) State Ethnic Affairs Commission,Key Laboratory of Electronic and Information Engineering,Chengdu,China","institution_ids":["https://openalex.org/I2799449666"]},{"raw_affiliation_string":"Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University) State Ethnic Affairs Commission, Chengdu, China","institution_ids":["https://openalex.org/I2799449666","https://openalex.org/I6645349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101670319","display_name":"Wei Xiang","orcid":"https://orcid.org/0000-0002-4297-7034"},"institutions":[{"id":"https://openalex.org/I2799449666","display_name":"State Ethnic Affairs Commission","ror":"https://ror.org/01p9g6b97","country_code":"CN","type":"government","lineage":["https://openalex.org/I2799449666"]},{"id":"https://openalex.org/I6645349","display_name":"Southwest Minzu University","ror":"https://ror.org/04gaexw88","country_code":"CN","type":"education","lineage":["https://openalex.org/I6645349"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Xiang","raw_affiliation_strings":["(Southwest Minzu University) State Ethnic Affairs Commission,Key Laboratory of Electronic and Information Engineering,Chengdu,China","Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University) State Ethnic Affairs Commission, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(Southwest Minzu University) State Ethnic Affairs Commission,Key Laboratory of Electronic and Information Engineering,Chengdu,China","institution_ids":["https://openalex.org/I2799449666"]},{"raw_affiliation_string":"Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University) State Ethnic Affairs Commission, Chengdu, China","institution_ids":["https://openalex.org/I2799449666","https://openalex.org/I6645349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.44446269,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1093","last_page":"1097"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9970999956130981,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.797946572303772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7742950320243835},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.7736947536468506},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6972853541374207},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6949902772903442},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6637758016586304},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6221804618835449},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5425591468811035},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5203875303268433},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5092223882675171},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4922715723514557},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4429200291633606},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41331180930137634},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20975139737129211},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08802184462547302}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.797946572303772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742950320243835},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.7736947536468506},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6972853541374207},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6949902772903442},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6637758016586304},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6221804618835449},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5425591468811035},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5203875303268433},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5092223882675171},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4922715723514557},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4429200291633606},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41331180930137634},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20975139737129211},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08802184462547302},{"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iske47853.2019.9170374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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":21,"referenced_works":["https://openalex.org/W1965947362","https://openalex.org/W1977821862","https://openalex.org/W1990937109","https://openalex.org/W1998294030","https://openalex.org/W2038952578","https://openalex.org/W2082308025","https://openalex.org/W2101866605","https://openalex.org/W2136000821","https://openalex.org/W2145287260","https://openalex.org/W2151103935","https://openalex.org/W2152826865","https://openalex.org/W2156387975","https://openalex.org/W2605701576","https://openalex.org/W2623892189","https://openalex.org/W2770121394","https://openalex.org/W2962819150","https://openalex.org/W6644760338","https://openalex.org/W6649778027","https://openalex.org/W6681342084","https://openalex.org/W6682889407","https://openalex.org/W6736221584"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W4243161226","https://openalex.org/W2950647290","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W1968481813","https://openalex.org/W2985118265"],"abstract_inverted_index":{"Face":[0,44],"recognition":[1],"has":[2],"made":[3],"great":[4],"progress":[5],"due":[6],"to":[7,84,100,129,164,220],"the":[8,58,65,69,73,77,94,102,115,122,138,146,155,166,192,195,223,227,236,241,251,255],"development":[9],"of":[10,22,50,53,60,148,160,169,181,194,200,229,257],"convolutional":[11],"neural":[12,112,171],"network":[13,74,172],"(CNN),":[14],"and":[15,30,40,86,118,157,175,240],"face":[16,116,198,207],"alignment":[17],"is":[18,25,82,218],"an":[19],"important":[20],"part":[21,141],"recognition,":[23],"it":[24,162],"easily":[26,83],"affected":[27],"by":[28,90,107,125,233,247],"gestures":[29],"occlusion.":[31],"In":[32],"this":[33,258],"paper,":[34],"we":[35],"propose":[36],"Residual":[37,78],"Neural":[38,79],"Network":[39,46],"Wing":[41],"Loss":[42],"for":[43],"Alignment":[45],"(RWAN),":[47],"which":[48,81,225],"consists":[49],"a":[51,211],"plurality":[52],"stages,":[54],"each":[55],"stage":[56],"ameliorates":[57],"position":[59],"facial":[61],"landmarks":[62],"estimated":[63],"from":[64,121],"previous":[66],"stage.":[67],"Unlike":[68],"traditional":[70],"cascading":[71],"model,":[72],"model":[75,196],"uses":[76,98],"Network,":[80],"optimize":[85],"can":[87],"improve":[88,165],"accuracy":[89,193],"adding":[91],"considerable":[92],"depth,":[93],"internal":[95],"residual":[96],"block":[97],"shortcut":[99],"alleviate":[101],"gradient":[103],"explosion":[104],"problem":[105,180,228],"caused":[106],"increasing":[108],"depth":[109],"in":[110,137,206],"deep":[111,170],"network.":[113],"Enter":[114],"images":[117],"extract":[119],"features":[120],"entire":[123],"image":[124],"introducing":[126],"landmark":[127,147],"heatmaps":[128],"obtain":[130],"more":[131],"accurate":[132],"positioning.":[133],"Using":[134],"wing":[135],"loss":[136,139],"function":[140],"not":[142,184,204],"only":[143,185],"focuses":[144],"on":[145,154,250],"large":[149],"error":[150],"points,":[151],"but":[152,189,213],"also":[153,190],"small":[156,174],"medium":[158,176],"errors":[159],"landmarks,":[161],"aims":[163],"training":[167,237],"ability":[168],"with":[173,222],"range":[177],"error.":[178],"The":[179,243],"unbalanced":[182,230],"data":[183,215,231],"confuses":[186],"classification":[187],"tasks,":[188],"affects":[191],"when":[197],"samples":[199],"different":[201],"attitudes":[202],"are":[203],"balanced":[205],"key":[208],"detection":[209],"tasks.":[210],"simple":[212],"effective":[214],"enhancement":[216],"method":[217,249],"proposed":[219],"deal":[221],"problem,":[224],"solves":[226],"processing":[232],"randomly":[234],"rotating":[235],"samples,":[238],"amplifying":[239],"like.":[242],"experimental":[244],"results":[245],"obtained":[246],"our":[248],"300W":[252],"dataset":[253],"indicate":[254],"advantages":[256],"method.":[259]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}