{"id":"https://openalex.org/W2970277727","doi":"https://doi.org/10.1109/icip.2019.8803754","title":"Multi-View Geometric Mean Metric Learning for Kinship Verification","display_name":"Multi-View Geometric Mean Metric Learning for Kinship Verification","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970277727","doi":"https://doi.org/10.1109/icip.2019.8803754","mag":"2970277727"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803754","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5043477487","display_name":"Junlin Hu","orcid":"https://orcid.org/0000-0002-0117-3494"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junlin Hu","raw_affiliation_strings":["Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100460385","display_name":"Jiwen Lu","orcid":"https://orcid.org/0000-0002-6121-5529"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwen Lu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418783","display_name":"Li Liu","orcid":"https://orcid.org/0000-0002-2011-2873"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Liu","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100620306","display_name":"Jie Zhou","orcid":"https://orcid.org/0000-0001-7701-234X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zhou","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043477487"],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":1.6196,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.87266193,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1178","last_page":"1182"},"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/T10828","display_name":"Biometric Identification and Security","score":0.9487000107765198,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9390000104904175,"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/kinship","display_name":"Kinship","score":0.7589521408081055},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7431958913803101},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6808500289916992},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6716089844703674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.644023060798645},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.637241780757904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6335045695304871},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5114668607711792},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4590619206428528},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3685801327228546},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.285826712846756},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.05943608283996582}],"concepts":[{"id":"https://openalex.org/C144348335","wikidata":"https://www.wikidata.org/wiki/Q171318","display_name":"Kinship","level":2,"score":0.7589521408081055},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7431958913803101},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6808500289916992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6716089844703674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.644023060798645},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.637241780757904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6335045695304871},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5114668607711792},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4590619206428528},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3685801327228546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.285826712846756},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.05943608283996582},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803754","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1488199789","https://openalex.org/W1655697160","https://openalex.org/W1915668717","https://openalex.org/W1998230826","https://openalex.org/W2087128748","https://openalex.org/W2106053110","https://openalex.org/W2108909793","https://openalex.org/W2117154949","https://openalex.org/W2122399564","https://openalex.org/W2132886138","https://openalex.org/W2145773134","https://openalex.org/W2151103935","https://openalex.org/W2164322079","https://openalex.org/W2169495281","https://openalex.org/W2468075526","https://openalex.org/W2560852071","https://openalex.org/W2604305041","https://openalex.org/W2679426462","https://openalex.org/W2797200251","https://openalex.org/W2800740410","https://openalex.org/W2804093170","https://openalex.org/W2913071101","https://openalex.org/W3101857744","https://openalex.org/W4294338724","https://openalex.org/W6629240041","https://openalex.org/W6675751002","https://openalex.org/W6677328822","https://openalex.org/W6678259400","https://openalex.org/W6719959352","https://openalex.org/W7002266192"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2046291598","https://openalex.org/W2504703202","https://openalex.org/W2094920358","https://openalex.org/W2890085216","https://openalex.org/W2041448692","https://openalex.org/W4317827232","https://openalex.org/W2247121321","https://openalex.org/W1708418756","https://openalex.org/W2049930962"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,45,68],"multi-view":[4],"geometric":[5,87,91],"mean":[6,88,92],"metric":[7,93],"learning":[8],"(MvGMML)":[9],"method":[10,82],"for":[11,94],"the":[12,59,120],"real-world":[13],"kinship":[14,21,41,114],"verification":[15,22,42],"from":[16],"facial":[17,30,46,54],"images.":[18],"Unlike":[19],"existing":[20],"methods":[23],"which":[24],"dramatically":[25],"degrade":[26],"their":[27],"performance":[28],"when":[29],"images":[31],"are":[32,116],"not":[33],"well":[34],"aligned,":[35],"we":[36],"present":[37],"an":[38],"efficient":[39],"misalignment-robust":[40],"framework.":[43],"First,":[44],"feature":[47,55,76,98],"detector":[48],"is":[49,72],"employed":[50],"to":[51,100,118],"localize":[52],"several":[53],"points":[56],"such":[57],"as":[58],"right":[60],"and":[61],"left":[62],"corners":[63],"of":[64,105,122],"two":[65,111],"eyes.":[66],"Then,":[67],"dense":[69],"SIFT":[70],"descriptor":[71],"extracted":[73],"around":[74],"each":[75,95],"point.":[77],"Lastly,":[78],"our":[79,123],"proposed":[80],"MvGMML":[81],"jointly":[83],"learns":[84],"multiple":[85],"local":[86],"metrics,":[89],"one":[90],"view":[96],"(i.e.,":[97],"point),":[99],"better":[101],"exploit":[102],"complementary":[103],"information":[104],"all":[106],"views.":[107],"Experimental":[108],"results":[109],"on":[110],"widely":[112],"used":[113],"datasets":[115],"presented":[117],"show":[119],"efficacy":[121],"method.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
