{"id":"https://openalex.org/W2997745647","doi":"https://doi.org/10.1109/icct46805.2019.8947113","title":"3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture","display_name":"3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2997745647","doi":"https://doi.org/10.1109/icct46805.2019.8947113","mag":"2997745647"},"language":"en","primary_location":{"id":"doi:10.1109/icct46805.2019.8947113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct46805.2019.8947113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","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/A5038472644","display_name":"Kangming Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kangming Xu","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023643105","display_name":"Xianmei Wang","orcid":"https://orcid.org/0000-0002-6273-3096"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianmei Wang","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101891849","display_name":"Zhenghua Hu","orcid":"https://orcid.org/0000-0003-4316-776X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenghua Hu","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404244","display_name":"Zihao Zhang","orcid":"https://orcid.org/0000-0003-0808-220X"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Zhang","raw_affiliation_strings":["Center of AI and Intelligent Operation R&D, China Mobile Research & Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Center of AI and Intelligent Operation R&D, China Mobile Research & Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5038472644"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":1.0122,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.81327072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9975000023841858,"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":0.9747999906539917,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8334683179855347},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6941487789154053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6762625575065613},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6664363741874695},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6636055111885071},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6430799961090088},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5950137376785278},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.569251298904419},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5255138874053955},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5024123191833496},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4816121459007263},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.47597092390060425},{"id":"https://openalex.org/keywords/texture-mapping","display_name":"Texture mapping","score":0.4418239891529083},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.44168853759765625},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.43520307540893555},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3310067653656006},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1984064280986786}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8334683179855347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6941487789154053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6762625575065613},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6664363741874695},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6636055111885071},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6430799961090088},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5950137376785278},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.569251298904419},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5255138874053955},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5024123191833496},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4816121459007263},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.47597092390060425},{"id":"https://openalex.org/C200585589","wikidata":"https://www.wikidata.org/wiki/Q752176","display_name":"Texture mapping","level":2,"score":0.4418239891529083},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.44168853759765625},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.43520307540893555},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3310067653656006},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1984064280986786},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icct46805.2019.8947113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct46805.2019.8947113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","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":10,"referenced_works":["https://openalex.org/W2106047956","https://openalex.org/W2114588272","https://openalex.org/W2136884564","https://openalex.org/W2152492602","https://openalex.org/W2770446278","https://openalex.org/W2963721962","https://openalex.org/W2963865528","https://openalex.org/W6676031134","https://openalex.org/W6680562432","https://openalex.org/W6682465216"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W2734518707","https://openalex.org/W3171672727","https://openalex.org/W2082228984","https://openalex.org/W2073819617"],"abstract_inverted_index":{"Massive":[0],"amount":[1],"of":[2,131,141,149],"training":[3,144],"samples":[4],"is":[5,43,89,107,152],"a":[6,19,40,81],"challenge":[7],"for":[8,27],"3D":[9,28,34,52,61,73,104],"face":[10,29,33,53,93,105],"recognition":[11,30,106,129,147],"using":[12,142],"deep":[13,23],"learning":[14],"frame.":[15],"This":[16],"paper":[17],"shows":[18],"method":[20,133,151],"that":[21],"uses":[22],"twin":[24,111],"neural":[25,112],"network":[26],"by":[31,75,109],"blending":[32],"depth":[35,41,96],"and":[36,67,95],"2D":[37,65],"texture.":[38],"First,":[39],"map":[42,59],"generated.":[44],"In":[45],"order":[46],"to":[47,72,91],"repair":[48],"holes":[49],"in":[50,138],"the":[51,76,124,128,139,146],"model":[54,84],"with":[55,85,123],"low":[56],"complexity,":[57],"we":[58],"those":[60],"hole":[62],"points":[63],"into":[64],"plane,":[66],"then":[68],"reverse":[69],"them":[70],"back":[71],"space":[74],"least":[77],"square":[78],"rule.":[79],"Second,":[80],"convolution":[82],"kernel":[83],"two":[86],"layer":[87],"channels":[88],"used":[90],"fuse":[92],"image":[94],"image.":[97],"Finally,":[98],"after":[99],"sample":[100],"pairs":[101],"are":[102],"generated,":[103],"performed":[108],"convolutional":[110],"network.":[113],"The":[114],"experimental":[115],"results":[116],"on":[117],"CASIA-3D":[118],"dataset":[119],"show":[120],"that,":[121],"compared":[122],"classical":[125],"CNN":[126],"method,":[127],"accuracy":[130],"our":[132,150],"increases":[134],"about":[135,153],"2.85%.":[136],"And":[137],"case":[140],"small":[143],"sets,":[145],"rate":[148],"4%":[154],"higher.":[155]},"counts_by_year":[{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
