{"id":"https://openalex.org/W2752198762","doi":"https://doi.org/10.1109/icmew.2017.8026288","title":"Discriminative metric learning for video-based kinship verification","display_name":"Discriminative metric learning for video-based kinship verification","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2752198762","doi":"https://doi.org/10.1109/icmew.2017.8026288","mag":"2752198762"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5011536717","display_name":"Haibin Yan","orcid":"https://orcid.org/0000-0003-0811-6545"},"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":"Haibin Yan","raw_affiliation_strings":["School of Automation, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5011536717"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11811831,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"645","last_page":"650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9904999732971191,"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":0.9713000059127808,"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/kinship","display_name":"Kinship","score":0.9282104969024658},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8372293710708618},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7680746912956238},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.745037317276001},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7435113191604614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5981934666633606},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5011003017425537},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.497008353471756},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4416349232196808},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34910377860069275},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.062035441398620605},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.0580771267414093}],"concepts":[{"id":"https://openalex.org/C144348335","wikidata":"https://www.wikidata.org/wiki/Q171318","display_name":"Kinship","level":2,"score":0.9282104969024658},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8372293710708618},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7680746912956238},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.745037317276001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7435113191604614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5981934666633606},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5011003017425537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.497008353471756},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4416349232196808},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34910377860069275},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.062035441398620605},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0580771267414093},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2017.8026288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W73315491","https://openalex.org/W1488199789","https://openalex.org/W1498305593","https://openalex.org/W1587780933","https://openalex.org/W1593040460","https://openalex.org/W1849007038","https://openalex.org/W1900408618","https://openalex.org/W1985620681","https://openalex.org/W1991452654","https://openalex.org/W1992378626","https://openalex.org/W1996337501","https://openalex.org/W1998230826","https://openalex.org/W1998667615","https://openalex.org/W2012783213","https://openalex.org/W2030027397","https://openalex.org/W2047186200","https://openalex.org/W2048110836","https://openalex.org/W2057510448","https://openalex.org/W2061098512","https://openalex.org/W2062292812","https://openalex.org/W2068042582","https://openalex.org/W2076434944","https://openalex.org/W2079742827","https://openalex.org/W2090504921","https://openalex.org/W2099263919","https://openalex.org/W2108909793","https://openalex.org/W2109824782","https://openalex.org/W2112295098","https://openalex.org/W2112999064","https://openalex.org/W2122399564","https://openalex.org/W2125911081","https://openalex.org/W2129785219","https://openalex.org/W2132886138","https://openalex.org/W2135280105","https://openalex.org/W2145773134","https://openalex.org/W2161969291","https://openalex.org/W2163808566","https://openalex.org/W2164322079","https://openalex.org/W2169495281","https://openalex.org/W2172260703","https://openalex.org/W2560852071","https://openalex.org/W6602994362","https://openalex.org/W6629240041","https://openalex.org/W6629622560","https://openalex.org/W6635689683","https://openalex.org/W6638943231","https://openalex.org/W6678259400","https://openalex.org/W6681440187"],"related_works":["https://openalex.org/W1579373186","https://openalex.org/W4312685612","https://openalex.org/W2046291598","https://openalex.org/W4317827232","https://openalex.org/W2890085216","https://openalex.org/W2504703202","https://openalex.org/W4252628068","https://openalex.org/W1482441085","https://openalex.org/W2966858528","https://openalex.org/W2151687600"],"abstract_inverted_index":{"In":[0,45],"this":[1,46],"paper,":[2,47],"we":[3,48,60,97],"investigate":[4],"the":[5,31,43,50,72,82,90,94,115,122],"problem":[6,32],"of":[7,33,117,124],"video-based":[8,34,54,83,106],"kinship":[9,23,35,55,84,107],"verification":[10,24,36,85],"using":[11],"discriminative":[12],"metric":[13,102,126],"learning.":[14],"While":[15],"several":[16],"attempts":[17],"have":[18],"been":[19,39],"made":[20],"on":[21],"facial":[22],"from":[25],"still":[26],"images,":[27],"to":[28,53,113],"our":[29,118],"knowledge,":[30],"has":[37],"not":[38],"formally":[40],"addressed":[41],"in":[42,71,78],"literature.":[44],"make":[49],"two":[51,99],"contributions":[52],"verification.":[56,108],"On":[57,93],"one":[58],"hand,":[59,96],"present":[61],"a":[62],"new":[63],"video":[64],"face":[65],"dataset":[66,120],"called":[67],"Kinship":[68],"Face":[69],"Videos":[70],"Wild":[73],"(KFVW)":[74],"which":[75],"were":[76],"captured":[77],"wild":[79],"conditions":[80],"for":[81,105],"study,":[86],"as":[87,89],"well":[88],"standard":[91],"benchmark.":[92],"other":[95],"employ":[98],"widely":[100],"used":[101],"learning":[103,127],"methods":[104],"Experimental":[109],"results":[110],"are":[111],"presented":[112],"demonstrate":[114],"efficacy":[116],"proposed":[119],"and":[121],"effectiveness":[123],"existing":[125],"methods.":[128]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}