{"id":"https://openalex.org/W2051949278","doi":"https://doi.org/10.1109/wacv.2014.6836099","title":"Exemplar codes for facial attributes and tattoo recognition","display_name":"Exemplar codes for facial attributes and tattoo recognition","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W2051949278","doi":"https://doi.org/10.1109/wacv.2014.6836099","mag":"2051949278"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2014.6836099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836099","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of Computer Vision","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/A5026971189","display_name":"Michael J. Wilber","orcid":"https://orcid.org/0000-0001-7040-0251"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael J. Wilber","raw_affiliation_strings":["Cornell University","Securics, Inc, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Securics, Inc, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077670684","display_name":"Ethan M. Rudd","orcid":"https://orcid.org/0000-0001-8831-5514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ethan Rudd","raw_affiliation_strings":["Securics, Inc","Securics, Inc, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Securics, Inc","institution_ids":[]},{"raw_affiliation_string":"Securics, Inc, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037199830","display_name":"Brian Heflin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brian Heflin","raw_affiliation_strings":["Securics, Inc","Securics, Inc, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Securics, Inc","institution_ids":[]},{"raw_affiliation_string":"Securics, Inc, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110099401","display_name":"Yui-Man Lui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yui-Man Lui","raw_affiliation_strings":["Securics, Inc","Securics, Inc, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Securics, Inc","institution_ids":[]},{"raw_affiliation_string":"Securics, Inc, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049661026","display_name":"Terrance E. Boult","orcid":"https://orcid.org/0000-0001-5007-2529"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Terrance E. Boult","raw_affiliation_strings":["Securics, Inc","Securics, Inc, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Securics, Inc","institution_ids":[]},{"raw_affiliation_string":"Securics, Inc, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9800000190734863,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9800000190734863,"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":0.9790999889373779,"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":0.9763000011444092,"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/computer-science","display_name":"Computer science","score":0.681923508644104},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5374316573143005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43829819560050964},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.29824182391166687}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.681923508644104},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5374316573143005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43829819560050964},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29824182391166687}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2014.6836099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836099","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1509928947","https://openalex.org/W1513886033","https://openalex.org/W1618905105","https://openalex.org/W1782590233","https://openalex.org/W1989684337","https://openalex.org/W1993747918","https://openalex.org/W2001519147","https://openalex.org/W2008932806","https://openalex.org/W2011646888","https://openalex.org/W2018006179","https://openalex.org/W2094852750","https://openalex.org/W2099017151","https://openalex.org/W2099453662","https://openalex.org/W2114942327","https://openalex.org/W2116750363","https://openalex.org/W2118696714","https://openalex.org/W2123460005","https://openalex.org/W2126448884","https://openalex.org/W2132287521","https://openalex.org/W2137991690","https://openalex.org/W2142947774","https://openalex.org/W2157439590","https://openalex.org/W2167057485","https://openalex.org/W2168996682","https://openalex.org/W2536626143","https://openalex.org/W2911964244","https://openalex.org/W6630306207","https://openalex.org/W6636501900","https://openalex.org/W6654967971","https://openalex.org/W6677469181","https://openalex.org/W6678412313","https://openalex.org/W6684482212"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"When":[0],"implementing":[1],"real-world":[2],"computer":[3],"vision":[4,44],"systems,":[5],"researchers":[6],"can":[7],"use":[8],"mid-level":[9,24,56],"representations":[10,25],"as":[11],"a":[12,53,123],"tool":[13],"to":[14,30,37,84,114],"adjust":[15],"the":[16,47,102,105],"trade-off":[17],"between":[18],"accuracy":[19,28,117],"and":[20,73,90,107],"efficiency.":[21],"Unfortunately,":[22],"existing":[23],"that":[26,58],"improve":[27],"tend":[29],"decrease":[31],"efficiency,":[32],"or":[33,43],"are":[34,68,99],"specifically":[35],"tailored":[36],"work":[38],"well":[39],"within":[40],"one":[41],"pipeline":[42],"problem":[45],"at":[46],"exclusion":[48],"of":[49,104],"others.":[50],"We":[51,80],"introduce":[52],"novel,":[54],"efficient":[55],"representation":[57],"improves":[59],"classification":[60],"efficiency":[61,109],"without":[62],"sacrificing":[63],"accuracy.":[64],"Our":[65],"Exemplar":[66,82,97],"Codes":[67,83,98],"based":[69],"on":[70,119],"linear":[71],"classifiers":[72],"probability":[74],"normalization":[75],"from":[76],"extreme":[77],"value":[78],"theory.":[79],"apply":[81],"two":[85],"problems:":[86],"facial":[87],"attribute":[88],"extraction":[89],"tattoo":[91],"classification.":[92],"In":[93],"these":[94],"settings,":[95],"our":[96],"competitive":[100],"with":[101,122],"state":[103],"art":[106],"offer":[108],"benefits,":[110],"making":[111],"it":[112],"possible":[113],"achieve":[115],"high":[116],"even":[118],"commodity":[120],"hardware":[121],"low":[124],"computational":[125],"budget.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
