{"id":"https://openalex.org/W2573716527","doi":"https://doi.org/10.1109/ispacs.2016.7824728","title":"Clothing-invariant gait recognition using convolutional neural network","display_name":"Clothing-invariant gait recognition using convolutional neural network","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2573716527","doi":"https://doi.org/10.1109/ispacs.2016.7824728","mag":"2573716527"},"language":"en","primary_location":{"id":"doi:10.1109/ispacs.2016.7824728","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispacs.2016.7824728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","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/A5090362942","display_name":"TzeWei Yeoh","orcid":null},"institutions":[{"id":"https://openalex.org/I137975476","display_name":"Shinshu University","ror":"https://ror.org/0244rem06","country_code":"JP","type":"education","lineage":["https://openalex.org/I137975476"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"TzeWei Yeoh","raw_affiliation_strings":["Faculty of Engineering, Shinshu University, Nagano, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Shinshu University, Nagano, Japan","institution_ids":["https://openalex.org/I137975476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036763177","display_name":"Hern\u00e1n Aguirre","orcid":"https://orcid.org/0000-0003-4480-1339"},"institutions":[{"id":"https://openalex.org/I137975476","display_name":"Shinshu University","ror":"https://ror.org/0244rem06","country_code":"JP","type":"education","lineage":["https://openalex.org/I137975476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hernan E. Aguirre","raw_affiliation_strings":["Faculty of Engineering, Shinshu University, Nagano, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Shinshu University, Nagano, Japan","institution_ids":["https://openalex.org/I137975476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101441960","display_name":"Kiyoshi Tanaka","orcid":"https://orcid.org/0000-0003-2174-6015"},"institutions":[{"id":"https://openalex.org/I137975476","display_name":"Shinshu University","ror":"https://ror.org/0244rem06","country_code":"JP","type":"education","lineage":["https://openalex.org/I137975476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kiyoshi Tanaka","raw_affiliation_strings":["Faculty of Engineering, Shinshu University, Nagano, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Shinshu University, Nagano, Japan","institution_ids":["https://openalex.org/I137975476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090362942"],"corresponding_institution_ids":["https://openalex.org/I137975476"],"apc_list":null,"apc_paid":null,"fwci":1.9239,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.85839299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9868999719619751,"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7741923332214355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7138060331344604},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.699313759803772},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6822469234466553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.674020528793335},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5857598781585693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5508013367652893},{"id":"https://openalex.org/keywords/clothing","display_name":"Clothing","score":0.5370591878890991},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4977774918079376},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4539276957511902},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.44608843326568604},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"Gait analysis","score":0.440510630607605},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.13526812195777893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1313830316066742}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7741923332214355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7138060331344604},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.699313759803772},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6822469234466553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.674020528793335},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5857598781585693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5508013367652893},{"id":"https://openalex.org/C530175646","wikidata":"https://www.wikidata.org/wiki/Q11460","display_name":"Clothing","level":2,"score":0.5370591878890991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4977774918079376},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4539276957511902},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.44608843326568604},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.440510630607605},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.13526812195777893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1313830316066742},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ispacs.2016.7824728","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispacs.2016.7824728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W28988658","https://openalex.org/W1546411676","https://openalex.org/W1570350587","https://openalex.org/W1667652561","https://openalex.org/W1849277567","https://openalex.org/W1963882359","https://openalex.org/W2022508996","https://openalex.org/W2029843939","https://openalex.org/W2030401579","https://openalex.org/W2037126576","https://openalex.org/W2054968871","https://openalex.org/W2080982032","https://openalex.org/W2088557251","https://openalex.org/W2117731089","https://openalex.org/W2123585936","https://openalex.org/W2126680226","https://openalex.org/W2158899491","https://openalex.org/W2163605009","https://openalex.org/W2168893862","https://openalex.org/W2170240176","https://openalex.org/W2194775991","https://openalex.org/W2275720868","https://openalex.org/W2294078083","https://openalex.org/W2546564278","https://openalex.org/W2557283755","https://openalex.org/W2952230511","https://openalex.org/W2963012544","https://openalex.org/W4302296459","https://openalex.org/W6632670727","https://openalex.org/W6637151318","https://openalex.org/W6639204139","https://openalex.org/W6683738474","https://openalex.org/W6684191040","https://openalex.org/W6685053522","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2133973503","https://openalex.org/W2471060339","https://openalex.org/W2148547327","https://openalex.org/W4226236273","https://openalex.org/W2125892956","https://openalex.org/W2130975749","https://openalex.org/W2493973380","https://openalex.org/W2394835211","https://openalex.org/W2809084995","https://openalex.org/W2729676947"],"abstract_inverted_index":{"Gait":[0,124],"recognition":[1,14,70,134],"is":[2,43,49],"recognizing":[3],"human":[4,59,69,94],"through":[5],"the":[6,13,21,39,44,93,103,130,140,166],"style":[7],"in":[8,157],"which":[9],"they":[10],"walk.":[11],"However,":[12,82],"task":[15],"can":[16,147],"become":[17],"complicated":[18],"due":[19],"to":[20,110,154],"existence":[22],"of":[23,57,105,135,165],"covariate":[24,40],"factors":[25],"(e.g.":[26],"clothing,":[27],"camera":[28],"viewpoint,":[29],"carrying":[30],"condition,":[31],"elapsed":[32],"time,":[33],"walking":[34],"surface,":[35],"etc).":[36],"Amongst":[37],"all":[38],"factors,":[41],"clothing":[42],"most":[45],"challenging":[46,67,131],"one.":[47],"This":[48],"because":[50],"it":[51,64],"may":[52],"obscure":[53],"a":[54],"significant":[55,77],"amount":[56],"discriminative":[58],"gait":[60,113,133],"features":[61,90,114],"and":[62,101],"makes":[63],"much":[65],"more":[66],"for":[68,91],"task.":[71],"In":[72,96],"recent,":[73],"there":[74],"has":[75],"been":[76],"research":[78],"on":[79,129],"this":[80,97],"problem.":[81],"conventional":[83,158],"state-of-the-art":[84,159],"methods":[85,160],"have":[86],"mostly":[87],"use":[88,104],"hand-crafted":[89,155],"representing":[92],"gait.":[95],"work,":[98],"we":[99],"explore":[100],"study":[102],"convolutional":[106],"neural":[107],"networks":[108],"(CNN)":[109],"automatically":[111],"learn":[112],"or":[115],"representations":[116],"directly":[117],"from":[118],"low-level":[119],"input":[120],"raw":[121],"data":[122],"(i.e.":[123],"Energy":[125],"Image":[126],"(GEI)).":[127],"Evaluations":[128],"clothing-invariant":[132],"OU-ISIR":[136],"Treadmill":[137],"dataset":[138],"B,":[139],"experiment":[141],"results":[142],"shows":[143],"that":[144],"our":[145],"method":[146],"achieve":[148],"far":[149],"better":[150],"performance":[151],"as":[152],"compared":[153],"feature":[156],"with":[161],"minimal":[162],"preprocessing":[163],"knowledge":[164],"problem":[167],"are":[168],"required.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}