{"id":"https://openalex.org/W2201824366","doi":null,"title":"Learning Multi-channel Deep Feature Representations for Face Recognition","display_name":"Learning Multi-channel Deep Feature Representations for Face Recognition","publication_year":2015,"publication_date":"2015-12-08","ids":{"openalex":"https://openalex.org/W2201824366","mag":"2201824366"},"language":"en","primary_location":{"id":"mag:2201824366","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v44/chen15learning.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xuewen Chen","orcid":"https://orcid.org/0000-0002-0392-3551","observed_orcids":["https://orcid.org/0000-0002-0392-3551"]},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuewen Chen","raw_affiliation_strings":["Wayne State University, Detroit, United States"],"raw_orcid":"https://orcid.org/0000-0002-0392-3551","affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, United States","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032612774","display_name":"Melih S. Aslan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Melih S. Aslan","raw_affiliation_strings":["Wayne State University, Detroit, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, United States","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055654445","display_name":"Kunlei Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunlei Zhang","raw_affiliation_strings":["Wayne State University, Detroit, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, United States","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101457342","display_name":"Thomas S. Huang","orcid":"https://orcid.org/0000-0001-8474-5859","observed_orcids":["https://orcid.org/0000-0001-8474-5859"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas S. Huang","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana, United States"],"raw_orcid":"https://orcid.org/0000-0001-8474-5859","affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana, United States","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6019,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.69473076,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"60","last_page":"71"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.5522000193595886,"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/T10057","display_name":"Face and Expression Recognition","score":0.5522000193595886,"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/T11448","display_name":"Face recognition and analysis","score":0.44359999895095825,"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/T11094","display_name":"Face Recognition and Perception","score":0.0010999999940395355,"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/face-recognition","display_name":"face recognition","score":0.9679999947547913},{"id":"https://openalex.org/keywords/deep-feature-learning","display_name":"deep feature learning","score":0.8069999814033508},{"id":"https://openalex.org/keywords/unsupervised-representation-learning","display_name":"unsupervised feature learning","score":0.7710000276565552},{"id":"https://openalex.org/keywords/supervised-feature-learning","display_name":"supervised feature learning","score":0.8669999837875366},{"id":"https://openalex.org/keywords/facial-region","display_name":"facial region","score":0.6959999799728394},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.8220000267028809},{"id":"https://openalex.org/keywords/discriminative-features","display_name":"discriminative features","score":0.8159999847412109}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.78666090965271},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7768235206604004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459946274757385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6799582839012146},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6574997305870056},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6398125290870667},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5895453691482544},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5793581008911133},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5749572515487671},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5706771612167358},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5011107921600342},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.44250616431236267},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43886598944664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36512303352355957},{"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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2201824366","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v44/chen15learning.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1502160187","https://openalex.org/W1665214252","https://openalex.org/W1904365287","https://openalex.org/W1975056068","https://openalex.org/W1982048725","https://openalex.org/W1988631628","https://openalex.org/W2005286252","https://openalex.org/W2025711495","https://openalex.org/W2056567769","https://openalex.org/W2077628181","https://openalex.org/W2085400714","https://openalex.org/W2096733369","https://openalex.org/W2097486709","https://openalex.org/W2100495367","https://openalex.org/W2116064496","https://openalex.org/W2121647436","https://openalex.org/W2123921160","https://openalex.org/W2132467081","https://openalex.org/W2136540140","https://openalex.org/W2136922672","https://openalex.org/W2138451337","https://openalex.org/W2144172034","https://openalex.org/W2145287260","https://openalex.org/W2148154194","https://openalex.org/W2151103935","https://openalex.org/W2153169342","https://openalex.org/W2158213899","https://openalex.org/W2161969291","https://openalex.org/W2163808566","https://openalex.org/W2163922914","https://openalex.org/W2165731615","https://openalex.org/W2536626143","https://openalex.org/W2994340921"],"related_works":["https://openalex.org/W2057755039","https://openalex.org/W3013509829","https://openalex.org/W2276144258","https://openalex.org/W2944724518","https://openalex.org/W2558233985","https://openalex.org/W2963640592","https://openalex.org/W2770601096","https://openalex.org/W2794802664","https://openalex.org/W2530216401","https://openalex.org/W2931374629"],"abstract_inverted_index":{"Deep":[0],"learning":[1,31],"provides":[2],"a":[3,33],"natural":[4],"way":[5],"to":[6,22,36,71,76],"obtain":[7,77],"feature":[8,25,90],"representations":[9,26,81,91],"from":[10,63],"data":[11,57],"without":[12],"relying":[13],"on":[14,94],"hand-crafted":[15],"descriptors.":[16],"In":[17],"this":[18],"paper,":[19],"we":[20],"propose":[21],"learn":[23,59],"deep":[24,79],"using":[27],"unsupervised":[28],"and":[29,58,101,103],"supervised":[30],"in":[32],"cascaded":[34],"fashion":[35],"produce":[37],"generically":[38],"descriptive":[39],"yet":[40],"class":[41],"specic":[42],"features.":[43],"The":[44,85],"proposed":[45,89],"method":[46],"can":[47],"take":[48],"full":[49],"advantage":[50],"of":[51,54,87],"the":[52,88],"availability":[53],"large-scale":[55],"unlabeled":[56],"discriminative":[60],"features":[61,65],"(supervised)":[62],"generic":[64],"(unsupervised).":[66],"It":[67],"is":[68,92],"then":[69],"applied":[70],"multiple":[72],"essential":[73],"facial":[74,80],"regions":[75],"multi-channel":[78],"for":[82],"face":[83,107],"recognition.":[84],"ecacy":[86],"validated":[93],"both":[95],"controlled":[96],"(i.e.,":[97],"extended":[98],"Yale-B,":[99],"Yale,":[100],"AR)":[102],"uncontrolled":[104],"(PubFig)":[105],"benchmark":[106],"databases.":[108],"Experimental":[109],"results":[110],"show":[111],"its":[112],"eectiveness.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}
