{"id":"https://openalex.org/W2750036380","doi":"https://doi.org/10.1109/access.2017.2741223","title":"Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding for Face Recognition","display_name":"Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding for Face Recognition","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2750036380","doi":"https://doi.org/10.1109/access.2017.2741223","mag":"2750036380"},"language":"en","primary_location":{"id":"doi:10.1109/access.2017.2741223","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2741223","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2017.2741223","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112382553","display_name":"Jiuzhen Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiuzhen Liang","raw_affiliation_strings":["School of Information and Engineering, Changzhou University, Changzhou, 213164, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Engineering, Changzhou University, Changzhou, 213164, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418568","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0003-3957-7061"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA"],"raw_orcid":"https://orcid.org/0000-0003-3957-7061","affiliations":[{"raw_affiliation_string":"Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102693222","display_name":"Yunfei Yi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161599","display_name":"Hechi University","ror":"https://ror.org/05pjkyk24","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210161599"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Yi","raw_affiliation_strings":["School of Computer and Information, Hechi University, Guangxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information, Hechi University, Guangxi, China","institution_ids":["https://openalex.org/I4210161599"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058860085","display_name":"Xiuxiu Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuxiu Xu","raw_affiliation_strings":["School of Information and Engineering, Changzhou University, Changzhou, 213164, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Engineering, Changzhou University, Changzhou, 213164, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044585780","display_name":"Meng Ding","orcid":"https://orcid.org/0000-0003-3125-3847"},"institutions":[{"id":"https://openalex.org/I4210144447","display_name":"OMNI Institute","ror":"https://ror.org/044z3jj38","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210144447"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meng Ding","raw_affiliation_strings":["OMNI AI, Inc., Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"OMNI AI, Inc., Houston, TX, USA","institution_ids":["https://openalex.org/I4210144447"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.1085,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.86554813,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"5","issue":null,"first_page":"17201","last_page":"17212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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":0.998199999332428,"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/T10828","display_name":"Biometric Identification and Security","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7851756811141968},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.7851046323776245},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7567206621170044},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.7250512838363647},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.723174512386322},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6887918710708618},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.685835599899292},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6791524887084961},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6594293117523193},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5637314319610596},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5593466758728027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5541156530380249},{"id":"https://openalex.org/keywords/scatter-matrix","display_name":"Scatter matrix","score":0.49500054121017456},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46706423163414},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4152408540248871},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4039002060890198},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22199833393096924},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.16598168015480042},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.08521217107772827}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7851756811141968},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.7851046323776245},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7567206621170044},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.7250512838363647},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.723174512386322},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6887918710708618},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.685835599899292},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6791524887084961},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6594293117523193},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5637314319610596},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5593466758728027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5541156530380249},{"id":"https://openalex.org/C176917957","wikidata":"https://www.wikidata.org/wiki/Q7430596","display_name":"Scatter matrix","level":4,"score":0.49500054121017456},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46706423163414},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4152408540248871},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4039002060890198},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22199833393096924},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.16598168015480042},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.08521217107772827},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2017.2741223","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2741223","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:stars.library.ucf.edu:scopus2015-6371","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/5372","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"},{"id":"pmh:oai:doaj.org/article:d3b3c74e18ec4d7d94784a23e8a92c4d","is_oa":true,"landing_page_url":"https://doaj.org/article/d3b3c74e18ec4d7d94784a23e8a92c4d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 5, Pp 17201-17212 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2017.2741223","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2741223","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G891265210","display_name":null,"funder_award_id":"61170121","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320308380","display_name":"Yale University","ror":"https://ror.org/03v76x132"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W140282302","https://openalex.org/W384497013","https://openalex.org/W1506469597","https://openalex.org/W1509928947","https://openalex.org/W1540288699","https://openalex.org/W1591385104","https://openalex.org/W1782590233","https://openalex.org/W1979306890","https://openalex.org/W1986988203","https://openalex.org/W1987228135","https://openalex.org/W2016787531","https://openalex.org/W2037034832","https://openalex.org/W2044117142","https://openalex.org/W2053186076","https://openalex.org/W2055551805","https://openalex.org/W2063907144","https://openalex.org/W2078118580","https://openalex.org/W2080183044","https://openalex.org/W2086735993","https://openalex.org/W2088801701","https://openalex.org/W2092131162","https://openalex.org/W2092314591","https://openalex.org/W2095459639","https://openalex.org/W2097366995","https://openalex.org/W2098461404","https://openalex.org/W2098693229","https://openalex.org/W2102544846","https://openalex.org/W2103250033","https://openalex.org/W2117553576","https://openalex.org/W2118706687","https://openalex.org/W2121647436","https://openalex.org/W2130658544","https://openalex.org/W2154872931","https://openalex.org/W2159174312","https://openalex.org/W2166732168","https://openalex.org/W2343952097","https://openalex.org/W2495657566","https://openalex.org/W2518224564","https://openalex.org/W2519420704","https://openalex.org/W2568998735","https://openalex.org/W2600061660","https://openalex.org/W2994340921","https://openalex.org/W4285719527","https://openalex.org/W6605658064","https://openalex.org/W6630306207","https://openalex.org/W6635552349","https://openalex.org/W6674755931","https://openalex.org/W6675955514","https://openalex.org/W6682644385","https://openalex.org/W6683646768"],"related_works":["https://openalex.org/W2375574759","https://openalex.org/W2383239174","https://openalex.org/W3088634662","https://openalex.org/W117517268","https://openalex.org/W3162910294","https://openalex.org/W2539700568","https://openalex.org/W2931531042","https://openalex.org/W1489327846","https://openalex.org/W4287375746","https://openalex.org/W3124275785"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,40],"novel":[6],"bilateral":[7],"2-D":[8],"neighborhood":[9],"preserving":[10],"discriminant":[11],"embedding":[12,32,47],"for":[13,18],"supervised":[14],"linear":[15],"dimensionality":[16,128],"reduction":[17,129],"face":[19,25,56,100],"recognition.":[20],"It":[21],"directly":[22],"extracts":[23],"discriminative":[24],"features":[26],"from":[27],"images":[28],"based":[29,44,84],"on":[30,45,85,103],"graph":[31,46],"and":[33,61,79,96,108,117],"Fisher's":[34,86],"criterion.":[35,87],"The":[36,88,111],"proposed":[37,92,121],"method":[38,93,122],"is":[39,94],"manifold":[41],"learning":[42],"algorithm":[43],"criterion,":[48],"which":[49],"can":[50],"effectively":[51],"discover":[52],"the":[53,76,81,91,104,115,120,126],"underlying":[54],"nonlinear":[55],"data":[57],"structure.":[58],"Both":[59],"within-neighboring":[60],"between-neighboring":[62],"information":[63],"are":[64],"taken":[65],"into":[66],"account":[67],"to":[68],"seek":[69],"an":[70],"optimal":[71],"projection":[72],"matrix":[73],"by":[74],"minimizing":[75],"intra-class":[77],"scatter":[78,83],"maximizing":[80],"inter-class":[82],"performance":[89],"of":[90,119],"evaluated":[95],"compared":[97,124],"with":[98,125],"other":[99],"recognition":[101],"schemes":[102],"Yale,":[105],"PICS,":[106],"AR,":[107],"LFW":[109],"databases.":[110],"experiment":[112],"results":[113],"demonstrate":[114],"effectiveness":[116],"superiority":[118],"as":[123],"state-ofthe-art":[127],"algorithms.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
