{"id":"https:\/\/openalex.org\/W7125586962","doi":"https:\/\/doi.org\/10.1109\/access.2026.3657520","title":"3-D Shape Identity Matching Across Domains Using Shared Kernel Transform Learning With Laplacian\u2013Beltrami Operator","display_name":"3-D Shape Identity Matching Across Domains Using Shared Kernel Transform Learning With Laplacian\u2013Beltrami Operator","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https:\/\/openalex.org\/W7125586962","doi":"https:\/\/doi.org\/10.1109\/access.2026.3657520"},"language":null,"primary_location":{"id":"doi:10.1109\/access.2026.3657520","is_oa":true,"landing_page_url":"https:\/\/doi.org\/10.1109\/access.2026.3657520","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.2026.3657520","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https:\/\/openalex.org\/A5123773816","display_name":"Yu Wang","orcid":null},"institutions":[{"id":"https:\/\/openalex.org\/I25254941","display_name":"Beijing Normal University","ror":"https:\/\/ror.org\/022k4wk35","country_code":"CN","type":"education","lineage":["https:\/\/openalex.org\/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https:\/\/openalex.org\/I25254941"]}]},{"author_position":"last","author":{"id":"https:\/\/openalex.org\/A5123740311","display_name":"Dan Zhang","orcid":null},"institutions":[{"id":"https:\/\/openalex.org\/I20616075","display_name":"Qinghai Normal University","ror":"https:\/\/ror.org\/03az1t892","country_code":"CN","type":"education","lineage":["https:\/\/openalex.org\/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Zhang","raw_affiliation_strings":["School of Computer Science, Qinghai Normal University, Xining, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Qinghai Normal University, Xining, China","institution_ids":["https:\/\/openalex.org\/I20616075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https:\/\/openalex.org\/A5123773816"],"corresponding_institution_ids":["https:\/\/openalex.org\/I25254941"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36627291,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"14434","last_page":"14448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https:\/\/openalex.org\/T11448","display_name":"Face recognition and analysis","score":0.8909000158309937,"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":0.8909000158309937,"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":0.03799999877810478,"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\/T11094","display_name":"Face Recognition and Perception","score":0.0142000000923872,"subfield":{"id":"https:\/\/openalex.org\/subfields\/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https:\/\/openalex.org\/fields\/28","display_name":"Neuroscience"},"domain":{"id":"https:\/\/openalex.org\/domains\/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https:\/\/openalex.org\/keywords\/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6460000276565552},{"id":"https:\/\/openalex.org\/keywords\/kernel","display_name":"Kernel (algebra)","score":0.6359999775886536},{"id":"https:\/\/openalex.org\/keywords\/matching","display_name":"Matching (statistics)","score":0.604200005531311},{"id":"https:\/\/openalex.org\/keywords\/multilinear-map","display_name":"Multilinear map","score":0.4875999987125397},{"id":"https:\/\/openalex.org\/keywords\/feature","display_name":"Feature (linguistics)","score":0.45809999108314514},{"id":"https:\/\/openalex.org\/keywords\/feature-extraction","display_name":"Feature extraction","score":0.4571000039577484},{"id":"https:\/\/openalex.org\/keywords\/similarity","display_name":"Similarity (geometry)","score":0.412200003862381},{"id":"https:\/\/openalex.org\/keywords\/nonlinear-system","display_name":"Nonlinear system","score":0.37290000915527344},{"id":"https:\/\/openalex.org\/keywords\/operator","display_name":"Operator (biology)","score":0.3621000051498413}],"concepts":[{"id":"https:\/\/openalex.org\/C154945302","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q11660","display_name":"Artificial intelligence","level":1,"score":0.663100004196167},{"id":"https:\/\/openalex.org\/C153180895","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6460000276565552},{"id":"https:\/\/openalex.org\/C74193536","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6359999775886536},{"id":"https:\/\/openalex.org\/C41008148","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q21198","display_name":"Computer science","level":0,"score":0.6085000038146973},{"id":"https:\/\/openalex.org\/C165064840","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.604200005531311},{"id":"https:\/\/openalex.org\/C84392682","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q1952404","display_name":"Multilinear map","level":2,"score":0.4875999987125397},{"id":"https:\/\/openalex.org\/C2776401178","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45809999108314514},{"id":"https:\/\/openalex.org\/C52622490","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q1026626","display_name":"Feature extraction","level":2,"score":0.4571000039577484},{"id":"https:\/\/openalex.org\/C103278499","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.412200003862381},{"id":"https:\/\/openalex.org\/C158622935","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q660848","display_name":"Nonlinear system","level":2,"score":0.37290000915527344},{"id":"https:\/\/openalex.org\/C17020691","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q139677","display_name":"Operator (biology)","level":5,"score":0.3621000051498413},{"id":"https:\/\/openalex.org\/C2776879701","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.34769999980926514},{"id":"https:\/\/openalex.org\/C122280245","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q620622","display_name":"Kernel method","level":3,"score":0.32749998569488525},{"id":"https:\/\/openalex.org\/C106487976","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.311599999666214},{"id":"https:\/\/openalex.org\/C31972630","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q844240","display_name":"Computer vision","level":1,"score":0.3073999881744385},{"id":"https:\/\/openalex.org\/C140779682","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.29739999771118164},{"id":"https:\/\/openalex.org\/C33923547","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q395","display_name":"Mathematics","level":0,"score":0.2971000075340271},{"id":"https:\/\/openalex.org\/C11413529","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q8366","display_name":"Algorithm","level":1,"score":0.2955999970436096},{"id":"https:\/\/openalex.org\/C204241405","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.288100004196167},{"id":"https:\/\/openalex.org\/C134567657","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q193794","display_name":"Identity matrix","level":3,"score":0.27709999680519104},{"id":"https:\/\/openalex.org\/C176217482","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q860554","display_name":"Metric (unit)","level":2,"score":0.2757999897003174},{"id":"https:\/\/openalex.org\/C165443888","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q1482183","display_name":"Transformation matrix","level":3,"score":0.2736000120639801},{"id":"https:\/\/openalex.org\/C70518039","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27070000767707825},{"id":"https:\/\/openalex.org\/C2778355321","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q17079427","display_name":"Identity (music)","level":2,"score":0.2696000039577484},{"id":"https:\/\/openalex.org\/C83665646","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q42139305","display_name":"Feature vector","level":2,"score":0.26660001277923584},{"id":"https:\/\/openalex.org\/C108583219","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q197536","display_name":"Deep learning","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109\/access.2026.3657520","is_oa":true,"landing_page_url":"https:\/\/doi.org\/10.1109\/access.2026.3657520","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"}],"best_oa_location":{"id":"doi:10.1109\/access.2026.3657520","is_oa":true,"landing_page_url":"https:\/\/doi.org\/10.1109\/access.2026.3657520","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https:\/\/openalex.org\/W117482296","https:\/\/openalex.org\/W129703402","https:\/\/openalex.org\/W140930454","https:\/\/openalex.org\/W1963932623","https:\/\/openalex.org\/W1989833002","https:\/\/openalex.org\/W1992405901","https:\/\/openalex.org\/W2002932002","https:\/\/openalex.org\/W2004125077","https:\/\/openalex.org\/W2004465977","https:\/\/openalex.org\/W2017107803","https:\/\/openalex.org\/W2017692724","https:\/\/openalex.org\/W2023051229","https:\/\/openalex.org\/W2051448670","https:\/\/openalex.org\/W2061889259","https:\/\/openalex.org\/W2083436148","https:\/\/openalex.org\/W2090457307","https:\/\/openalex.org\/W2103975664","https:\/\/openalex.org\/W2114708262","https:\/\/openalex.org\/W2120398992","https:\/\/openalex.org\/W2129812935","https:\/\/openalex.org\/W2136898995","https:\/\/openalex.org\/W2141039087","https:\/\/openalex.org\/W2142940228","https:\/\/openalex.org\/W2149861038","https:\/\/openalex.org\/W2157272434","https:\/\/openalex.org\/W2157785665","https:\/\/openalex.org\/W2189938900","https:\/\/openalex.org\/W2728183739","https:\/\/openalex.org\/W2753163043","https:\/\/openalex.org\/W2784060524","https:\/\/openalex.org\/W2887609787","https:\/\/openalex.org\/W2891396148","https:\/\/openalex.org\/W2922179541","https:\/\/openalex.org\/W2963329337","https:\/\/openalex.org\/W2963505807","https:\/\/openalex.org\/W2963887746","https:\/\/openalex.org\/W2987025224","https:\/\/openalex.org\/W3034681945","https:\/\/openalex.org\/W3034798648","https:\/\/openalex.org\/W3108137971","https:\/\/openalex.org\/W3119971933","https:\/\/openalex.org\/W3184676435","https:\/\/openalex.org\/W3195466989","https:\/\/openalex.org\/W4235354651","https:\/\/openalex.org\/W4310444167","https:\/\/openalex.org\/W4311585161","https:\/\/openalex.org\/W4312472600","https:\/\/openalex.org\/W4391274653","https:\/\/openalex.org\/W4394625656","https:\/\/openalex.org\/W4398186444","https:\/\/openalex.org\/W4399691550","https:\/\/openalex.org\/W4404856766","https:\/\/openalex.org\/W4405036409","https:\/\/openalex.org\/W4405827195","https:\/\/openalex.org\/W4408354228"],"related_works":[],"abstract_inverted_index":{"Identity":[0],"matching":[1,31,49,145,187,197],"(ID":[2],"matching)":[3],"across":[4],"domains":[5],"using":[6],"skull":[7],"or":[8],"facial":[9],"features":[10],"is":[11,75,137,149],"a":[12,53,98,108],"challenging":[13],"task,":[14],"particularly":[15],"when":[16],"transitioning":[17],"from":[18],"homogeneous-domain":[19],"(face-face)":[20],"to":[21,70,90,139],"heterogeneous-domain":[22],"(skull-face).":[23],"To":[24],"mitigate":[25],"the":[26,34,45,61,95,115,128,134,141,144,153,158,163],"computational":[27],"errors":[28],"in":[29,127,181],"ID":[30,186],"caused":[32],"by":[33,77,151,157],"strong":[35],"dependence":[36],"on":[37,192],"sampling":[38,78],"locations":[39,79],"for":[40,65],"shape":[41,66,86,147],"feature":[42,67,131],"descriptions":[43],"and":[44,80,84,175,184,199],"limitations":[46],"of":[47,101,165],"linear":[48],"techniques,":[50],"we":[51,106],"propose":[52],"shared":[54,102],"kernel":[55,99,130],"transform":[56,103,109,159],"learning":[57,104],"(SKTL)":[58],"method":[59,167],"leveraging":[60],"Laplacian-Beltrami":[62],"operator":[63],"(LBO)":[64],"description,":[68],"referred":[69],"as":[71,121,196],"LBOSKTL.":[72],"The":[73],"LBO":[74,96,129],"unaffected":[76],"captures":[81],"both":[82],"global":[83],"local":[85],"information,":[87],"spanning":[88],"low":[89],"high-frequency":[91],"descriptions.":[92],"By":[93],"integrating":[94],"with":[97,124,189],"version":[100],"algorithm,":[105],"derive":[107],"matrix":[110,136],"via":[111],"nonlinear":[112,117],"ways,":[113],"addressing":[114],"complex":[116],"relationships":[118],"between":[119],"shapes":[120],"they":[122],"align":[123],"real-world":[125],"scenarios":[126],"space.":[132],"Subsequently,":[133],"Gram":[135],"employed":[138],"solve":[140],"problem.":[142],"In":[143],"process,":[146],"similarity":[148],"assessed":[150],"comparing":[152],"sparse":[154],"coefficient\/representation":[155],"induced":[156],"matrix.":[160],"We":[161],"validate":[162],"efficacy":[164],"our":[166],"through":[168],"three":[169],"datasets":[170],"\u2013":[171,177],"multilinear":[172],"model,":[173],"FaceWarehouse,":[174],"FaceScape":[176],"demonstrating":[178],"its":[179],"performance":[180],"3D":[182],"skull-face":[183],"face-face":[185],"tasks,":[188],"evaluation":[190],"based":[191],"quantitative":[193],"metrics":[194],"such":[195],"accuracy":[198],"F1":[200],"scores.":[201]},"counts_by_year":[],"updated_date":"2026-02-02T03:55:41.653505","created_date":"2026-01-25T00:00:00"}