{"id":"https://openalex.org/W2516800609","doi":"https://doi.org/10.1109/icme.2016.7552902","title":"One-shot deep neural network for pose and illumination normalization face recognition","display_name":"One-shot deep neural network for pose and illumination normalization face recognition","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2516800609","doi":"https://doi.org/10.1109/icme.2016.7552902","mag":"2516800609"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2016.7552902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552902","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","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/A5050070022","display_name":"Zhongjun Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhongjun Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025452586","display_name":"Weihong Deng","orcid":"https://orcid.org/0000-0001-5952-6996"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihong Deng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050070022"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":2.1711,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.91904274,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.998199999332428,"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":0.9979000091552734,"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/normalization","display_name":"Normalization (sociology)","score":0.8856789469718933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8486685752868652},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759475827217102},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6836018562316895},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6227638721466064},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6086537837982178},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5992343425750732},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5034665465354919},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4854198396205902},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4455752372741699},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.43552684783935547},{"id":"https://openalex.org/keywords/three-dimensional-face-recognition","display_name":"Three-dimensional face recognition","score":0.4251730442047119},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41541844606399536},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.27481889724731445}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.8856789469718933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8486685752868652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759475827217102},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6836018562316895},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6227638721466064},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6086537837982178},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5992343425750732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5034665465354919},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4854198396205902},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4455752372741699},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.43552684783935547},{"id":"https://openalex.org/C88799230","wikidata":"https://www.wikidata.org/wiki/Q3398329","display_name":"Three-dimensional face recognition","level":5,"score":0.4251730442047119},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41541844606399536},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.27481889724731445},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2016.7552902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552902","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","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":20,"referenced_works":["https://openalex.org/W1575014920","https://openalex.org/W1955369839","https://openalex.org/W2063718733","https://openalex.org/W2065520107","https://openalex.org/W2072128103","https://openalex.org/W2090767496","https://openalex.org/W2118304946","https://openalex.org/W2127182692","https://openalex.org/W2131024102","https://openalex.org/W2153169342","https://openalex.org/W2155893237","https://openalex.org/W2160126058","https://openalex.org/W2163605009","https://openalex.org/W2912990735","https://openalex.org/W4231109964","https://openalex.org/W4285719527","https://openalex.org/W6634407266","https://openalex.org/W6641173728","https://openalex.org/W6679513098","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2166031825","https://openalex.org/W2918845005","https://openalex.org/W2040482211","https://openalex.org/W2058325696","https://openalex.org/W325114128","https://openalex.org/W2185537520","https://openalex.org/W3108145925","https://openalex.org/W4387163326","https://openalex.org/W1517271056","https://openalex.org/W2545238862"],"abstract_inverted_index":{"Pose":[0],"and":[1,24,33,52,56,72,85,93],"illumination":[2,25,54,73,86,94],"are":[3],"considered":[4],"as":[5],"two":[6],"main":[7],"challenges":[8],"that":[9],"face":[10,19],"recognition":[11,20,113],"system":[12],"encounters.":[13],"In":[14],"this":[15],"paper,":[16],"we":[17,89],"consider":[18],"problem":[21],"across":[22],"pose":[23,71,84,92],"variations,":[26],"given":[27],"small":[28],"amount":[29],"of":[30,46,59,118],"training":[31,101],"samples":[32,55],"single":[34],"sample":[35],"per":[36],"gallery":[37],"(a.k.a.,":[38],"one":[39],"shot":[40],"classification).":[41],"We":[42],"combine":[43],"the":[44,57,83,116],"strength":[45],"3D":[47],"models":[48],"in":[49,62],"generating":[50],"multiviews":[51],"various":[53],"ability":[58],"deep":[60,79],"learning":[61,63],"non-linear":[64],"transformation,":[65],"which":[66],"is":[67],"very":[68],"suitable":[69],"for":[70],"normalization,":[74],"by":[75],"using":[76],"a":[77,91],"multi-task":[78],"neural":[80,96],"network.":[81],"By":[82],"augmentation":[87],"strategy,":[88],"train":[90],"normalization":[95],"network":[97],"with":[98],"much":[99],"less":[100],"data":[102],"compared":[103],"to":[104],"other":[105],"methods.":[106],"Experiments":[107],"on":[108],"MultiPIE":[109],"database":[110],"achieve":[111],"competitive":[112],"results,":[114],"demonstrating":[115],"effectiveness":[117],"proposed":[119],"method.":[120]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}