{"id":"https://openalex.org/W2940859857","doi":"https://doi.org/10.1109/vcip.2018.8698734","title":"Video-based Parent-Child Relationship Prediction","display_name":"Video-based Parent-Child Relationship Prediction","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2940859857","doi":"https://doi.org/10.1109/vcip.2018.8698734","mag":"2940859857"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2018.8698734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Visual Communications and Image Processing (VCIP)","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/A5014652342","display_name":"Ying Sun","orcid":"https://orcid.org/0000-0002-4224-341X"},"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":false,"raw_author_name":"Ying Sun","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024716590","display_name":"Jiachen Li","orcid":"https://orcid.org/0000-0002-3543-6088"},"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":false,"raw_author_name":"Jiachen Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047763645","display_name":"Yiwen Wei","orcid":"https://orcid.org/0000-0002-8675-9370"},"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":false,"raw_author_name":"Yiwen Wei","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","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":false,"raw_author_name":"Haibin Yan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"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":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","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/T11448","display_name":"Face recognition and analysis","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/T10057","display_name":"Face and Expression Recognition","score":0.9901999831199646,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9839000105857849,"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":0.8028824925422668},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.744635283946991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6815237402915955},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6799042820930481},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6400173306465149},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.628920316696167},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5463488698005676},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5180635452270508},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4445805549621582},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43147027492523193}],"concepts":[{"id":"https://openalex.org/C144348335","wikidata":"https://www.wikidata.org/wiki/Q171318","display_name":"Kinship","level":2,"score":0.8028824925422668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.744635283946991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6815237402915955},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6799042820930481},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6400173306465149},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.628920316696167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5463488698005676},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5180635452270508},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4445805549621582},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43147027492523193},{"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},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2018.8698734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Visual Communications and Image Processing (VCIP)","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":23,"referenced_works":["https://openalex.org/W170472577","https://openalex.org/W1573726329","https://openalex.org/W1782590233","https://openalex.org/W1998230826","https://openalex.org/W1998808035","https://openalex.org/W2007527975","https://openalex.org/W2102512156","https://openalex.org/W2118072670","https://openalex.org/W2145773134","https://openalex.org/W2157032359","https://openalex.org/W2169598467","https://openalex.org/W2325939864","https://openalex.org/W2500367141","https://openalex.org/W2592683830","https://openalex.org/W2594075324","https://openalex.org/W2796369587","https://openalex.org/W2806332870","https://openalex.org/W4299419711","https://openalex.org/W6638046521","https://openalex.org/W6649863312","https://openalex.org/W6700903540","https://openalex.org/W6750268447","https://openalex.org/W6752485613"],"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/W1708418756","https://openalex.org/W2976679446","https://openalex.org/W2384651879"],"abstract_inverted_index":{"In":[0,92,147],"this":[1,93,120,145,180],"paper,":[2,94],"we":[3,95,151,166],"investigate":[4],"the":[5,22,81,84,89,104,140,174,177],"problem":[6],"of":[7,33,56,124,130,179],"video-based":[8],"parent-child":[9,23,47,196],"relationship":[10,24,48,197],"prediction":[11,49],"via":[12],"human":[13,34,63],"face":[14,52],"analysis.":[15],"Most":[16],"existing":[17],"kinship":[18,37],"verification":[19],"methods":[20,45],"predict":[21],"from":[25,156],"single":[26,67,71],"images,":[27],"which":[28,107,187],"cannot":[29,75],"effectively":[30,76],"utilize":[31],"videos":[32,129],"faces":[35,64,86],"for":[36,46],"verification.":[38],"Recently,":[39],"there":[40],"have":[41],"been":[42],"a":[43,66,70,97,131,168],"few":[44],"based":[50],"on":[51],"videos,":[53],"but":[54],"all":[55],"them":[57],"only":[58],"perform":[59],"pairwise":[60],"comparisons":[61],"between":[62,65],"parent":[68],"and":[69,83,114,163],"child.":[72],"Thus,":[73],"they":[74],"combine":[77],"information":[78],"about":[79],"both":[80,110],"father's":[82],"mother's":[85],"when":[87],"judging":[88],"kin":[90],"relationship.":[91],"propose":[96],"new":[98],"dtaaset,":[99],"Familyship":[100],"Face":[101],"Videos":[102],"in":[103,111],"Wild":[105],"(FFVW),":[106],"was":[108],"captured":[109],"wild":[112],"conditions":[113],"standard":[115],"reference,":[116],"to":[117,172,195],"deal":[118],"with":[119],"issue.":[121],"The":[122],"inputs":[123],"FFVW":[125],"are":[126],"three":[127],"separate":[128],"family.":[132],"To":[133],"our":[134,137,148,190],"best":[135],"knowledge,":[136],"paper":[138],"is":[139,182],"first":[141],"attempt":[142],"at":[143],"addressing":[144],"problem.":[146],"pre-processing":[149],"step,":[150],"extract":[152],"four":[153],"key":[154],"frames":[155],"each":[157],"video,":[158],"before":[159],"doing":[160],"facial":[161],"recognition":[162],"alignment.":[164],"Finally,":[165],"use":[167],"convolutional":[169],"neural":[170],"network":[171],"make":[173],"prediction.":[175,198],"Overall,":[176],"effectiveness":[178],"approach":[181],"verified":[183],"by":[184],"experimental":[185],"results,":[186],"show":[188],"that":[189],"dataset":[191],"outperforms":[192],"previous":[193],"approaches":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
