{"id":"https://openalex.org/W3127043252","doi":"https://doi.org/10.1109/icoin50884.2021.9334007","title":"Skeleton Silhouette Based Disentangled Feature Extraction Network for Invariant Gait Recognition","display_name":"Skeleton Silhouette Based Disentangled Feature Extraction Network for Invariant Gait Recognition","publication_year":2021,"publication_date":"2021-01-13","ids":{"openalex":"https://openalex.org/W3127043252","doi":"https://doi.org/10.1109/icoin50884.2021.9334007","mag":"3127043252"},"language":"en","primary_location":{"id":"doi:10.1109/icoin50884.2021.9334007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin50884.2021.9334007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information Networking (ICOIN)","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/A5035087264","display_name":"Jae-Seok Yoo","orcid":"https://orcid.org/0000-0002-4686-0220"},"institutions":[{"id":"https://openalex.org/I161024014","display_name":"Kwangwoon University","ror":"https://ror.org/02e9zc863","country_code":"KR","type":"education","lineage":["https://openalex.org/I161024014"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae-Seok Yoo","raw_affiliation_strings":["School of Robotics, Kwangwoon University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Robotics, Kwangwoon University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I161024014"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014943644","display_name":"Kwang\u2010Hyun Park","orcid":"https://orcid.org/0000-0001-8816-2976"},"institutions":[{"id":"https://openalex.org/I161024014","display_name":"Kwangwoon University","ror":"https://ror.org/02e9zc863","country_code":"KR","type":"education","lineage":["https://openalex.org/I161024014"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwang-Hyun Park","raw_affiliation_strings":["School of Robotics, Kwangwoon University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Robotics, Kwangwoon University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I161024014"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161024014"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/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/T10812","display_name":"Human Pose and Action Recognition","score":0.9785000085830688,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.957099974155426,"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/silhouette","display_name":"Silhouette","score":0.9487982988357544},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7817267775535583},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.7107230424880981},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6858483552932739},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6551121473312378},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6543847918510437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6462816596031189},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.612735390663147},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4507645070552826},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16298538446426392},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.06939083337783813}],"concepts":[{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.9487982988357544},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7817267775535583},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7107230424880981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6858483552932739},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6551121473312378},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6543847918510437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6462816596031189},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.612735390663147},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4507645070552826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16298538446426392},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.06939083337783813},{"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/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/icoin50884.2021.9334007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin50884.2021.9334007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information Networking (ICOIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.75,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W90353859","https://openalex.org/W1522301498","https://openalex.org/W1596155770","https://openalex.org/W1971239273","https://openalex.org/W2026800967","https://openalex.org/W2050785999","https://openalex.org/W2058897916","https://openalex.org/W2080982032","https://openalex.org/W2104335344","https://openalex.org/W2118393783","https://openalex.org/W2126680226","https://openalex.org/W2161370397","https://openalex.org/W2619034550","https://openalex.org/W2793243914","https://openalex.org/W2807461033","https://openalex.org/W2946396216","https://openalex.org/W2962730651","https://openalex.org/W2963045453","https://openalex.org/W2963301258","https://openalex.org/W2963854019","https://openalex.org/W2964121744","https://openalex.org/W2965326519","https://openalex.org/W2977530922","https://openalex.org/W3046961188","https://openalex.org/W4285719527","https://openalex.org/W6603697666","https://openalex.org/W6631190155","https://openalex.org/W6675575696","https://openalex.org/W6677884823","https://openalex.org/W6738467200"],"related_works":["https://openalex.org/W120096811","https://openalex.org/W3185413894","https://openalex.org/W2923160319","https://openalex.org/W2945912943","https://openalex.org/W2124490647","https://openalex.org/W4254098118","https://openalex.org/W2587763979","https://openalex.org/W1905194803","https://openalex.org/W2137411393","https://openalex.org/W1923394858"],"abstract_inverted_index":{"Recently,":[0],"the":[1,68],"demand":[2],"for":[3],"biometric":[4],"recognition":[5,44,72,96],"systems":[6],"through":[7],"deep-learning":[8],"algorithms":[9],"has":[10,20],"increased,":[11],"and":[12,26,56,83],"related":[13],"researches":[14],"have":[15],"been":[16],"actively":[17],"underway.":[18],"Gait":[19,43],"its":[21,33],"unique":[22],"characteristics":[23],"like":[24],"fingerprints":[25],"is":[27],"receiving":[28],"much":[29],"attention":[30],"due":[31],"to":[32,35,46,60],"capability":[34],"obtain":[36],"from":[37,74],"a":[38,52,91],"long":[39],"distance":[40],"without":[41],"contact.":[42],"means":[45],"identify":[47],"an":[48],"individual":[49],"by":[50],"analyzing":[51],"person's":[53],"gait":[54,71,95],"patterns,":[55],"can":[57],"be":[58],"applied":[59],"various":[61],"fields":[62],"such":[63,77],"as":[64,78],"criminal":[65],"investigations.":[66],"However,":[67],"accuracy":[69],"of":[70,80],"suffers":[73],"external":[75],"factors":[76],"variation":[79],"view":[81],"angles":[82],"clothes.":[84],"To":[85],"address":[86],"this":[87],"problem,":[88],"we":[89],"propose":[90],"skeleton":[92],"silhouette-based":[93],"disentangled":[94],"network,":[97],"which":[98],"learns":[99],"view-invariant":[100],"features.":[101],"Experimental":[102],"results":[103],"on":[104],"CASIA-B":[105],"dataset":[106],"are":[107],"also":[108],"shown":[109],"comparing":[110],"with":[111],"previous":[112],"methods.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}