{"id":"https://openalex.org/W2978157729","doi":"https://doi.org/10.1109/ijcnn.2019.8852249","title":"View-Invariant Gait Recognition Based on Deterministic Learning and Knowledge Fusion","display_name":"View-Invariant Gait Recognition Based on Deterministic Learning and Knowledge Fusion","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978157729","doi":"https://doi.org/10.1109/ijcnn.2019.8852249","mag":"2978157729"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5023097295","display_name":"Muqing Deng","orcid":"https://orcid.org/0000-0002-7595-2861"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Muqing Deng","raw_affiliation_strings":["Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101669144","display_name":"Haonan Yang","orcid":"https://orcid.org/0000-0001-8692-8317"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haonan Yang","raw_affiliation_strings":["Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074063024","display_name":"Jiuwen Cao","orcid":"https://orcid.org/0000-0002-6480-5794"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiuwen Cao","raw_affiliation_strings":["Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001632589","display_name":"Xiaoreng Feng","orcid":"https://orcid.org/0000-0003-4004-2689"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaoreng Feng","raw_affiliation_strings":["Department of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023097295"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":0.3912,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.59389108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"40","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9610999822616577,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9549999833106995,"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/invariant","display_name":"Invariant (physics)","score":0.5992247462272644},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5938271284103394},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.534002423286438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5329668521881104},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5194632411003113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33750540018081665},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22520771622657776},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.1885167956352234}],"concepts":[{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5992247462272644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5938271284103394},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.534002423286438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5329668521881104},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5194632411003113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33750540018081665},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22520771622657776},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.1885167956352234},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/W993535811","https://openalex.org/W1972340877","https://openalex.org/W2030513483","https://openalex.org/W2058226207","https://openalex.org/W2059901520","https://openalex.org/W2078046692","https://openalex.org/W2103610282","https://openalex.org/W2104335344","https://openalex.org/W2133693888","https://openalex.org/W2138218001","https://openalex.org/W2148568366","https://openalex.org/W2154171558","https://openalex.org/W2160588587","https://openalex.org/W2322772590","https://openalex.org/W2340382781","https://openalex.org/W2343246984","https://openalex.org/W2405392835","https://openalex.org/W2522764072","https://openalex.org/W2587763979","https://openalex.org/W2805611490","https://openalex.org/W2963745697","https://openalex.org/W6675575696","https://openalex.org/W6679901780"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Deformation":[0],"of":[1,13,35,53,87,93,112,134,148,179],"gait":[2,14,41,81,94,113,125,149],"silhouettes":[3,47],"caused":[4],"by":[5,68,156,193],"different":[6,61,152],"view":[7,36,107,135],"angles":[8],"heavily":[9],"affects":[10],"the":[11,33,44,84,91,103,124,132,139,145,180,186,189,194],"performance":[12],"recognition.":[15,42],"In":[16],"this":[17],"paper,":[18],"a":[19,158],"new":[20],"method":[21],"based":[22],"on":[23],"deterministic":[24,76],"learning":[25,77],"and":[26,96,121,161,188],"knowledge":[27,111,147,173],"fusion":[28,174],"is":[29,65,97,115,141,154],"proposed":[30],"to":[31,99,102,130],"eliminate":[32],"effect":[34],"angle":[37],"for":[38,167],"efficient":[39],"view-invariant":[40],"First,":[43],"binarized":[45],"walking":[46],"are":[48],"characterized":[49],"with":[50],"three":[51],"kinds":[52],"time-varying":[54],"width":[55,63],"parameters.":[56],"The":[57,79,109],"nonlinear":[58],"dynamics":[59,82,92,114,150],"underlying":[60],"individuals'":[62],"parameters":[64],"effectively":[66],"approximated":[67],"radial":[69],"basis":[70],"function":[71],"(RBF)":[72],"neural":[73,163],"networks":[74,120],"through":[75],"algorithm.":[78],"extracted":[80,184],"captures":[83],"spatio-temporal":[85],"characteristics":[86,183],"human":[88,169],"walking,":[89],"represents":[90],"motion,":[95],"shown":[98],"be":[100,204],"insensitive":[101],"variance":[104],"across":[105],"various":[106],"angles.":[108],"learned":[110,146],"stored":[116],"in":[117,128],"constant":[118],"RBF":[119],"used":[122],"as":[123],"pattern.":[126],"Second,":[127],"order":[129],"handle":[131],"problem":[133],"change":[136],"no":[137],"matter":[138],"variation":[140],"small":[142],"or":[143],"large,":[144],"from":[151,185],"views":[153],"fused":[155],"constructing":[157],"deep":[159],"convolutional":[160],"recurrent":[162],"network":[164],"(CRNN)":[165],"model":[166],"later":[168],"identification":[170],"task.":[171],"This":[172],"strategy":[175],"can":[176,203],"take":[177],"advantage":[178],"encoded":[181],"local":[182],"CNN":[187],"long-term":[190],"dependencies":[191],"captured":[192],"RNN.":[195],"Experimental":[196],"results":[197],"show":[198],"that":[199],"promising":[200],"recognition":[201],"accuracy":[202],"achieved.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}