{"id":"https://openalex.org/W7133299422","doi":"https://doi.org/10.1109/ijcb65343.2025.11411492","title":"TDGait: Modeling Temporal Dynamics for Gait Recognition","display_name":"TDGait: Modeling Temporal Dynamics for Gait Recognition","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7133299422","doi":"https://doi.org/10.1109/ijcb65343.2025.11411492"},"language":null,"primary_location":{"id":"doi:10.1109/ijcb65343.2025.11411492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","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/A5085567926","display_name":"Yuhao Zhou","orcid":"https://orcid.org/0000-0002-3347-1357"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhao Zhou","raw_affiliation_strings":["Northeast Forestry University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123516595","display_name":"Mingyang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyang Wang","raw_affiliation_strings":["Northeast Forestry University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Forestry University","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127953380","display_name":"Xianjie Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianjie Wang","raw_affiliation_strings":["Harbin Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55730002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.989799976348877,"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":0.989799976348877,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.000699999975040555,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6363000273704529},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5931000113487244},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5472999811172485},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.5422999858856201},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4975000023841858},{"id":"https://openalex.org/keywords/temporal-resolution","display_name":"Temporal resolution","score":0.47040000557899475},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45320001244544983},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.44510000944137573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7613999843597412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398000121116638},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6363000273704529},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.5422999858856201},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.47040000557899475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3873000144958496},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb65343.2025.11411492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","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":45,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1896402587","https://openalex.org/W2085058513","https://openalex.org/W2112796928","https://openalex.org/W2147800946","https://openalex.org/W2149516292","https://openalex.org/W2194775991","https://openalex.org/W2788751553","https://openalex.org/W2963155035","https://openalex.org/W2963180826","https://openalex.org/W2963301258","https://openalex.org/W2963524571","https://openalex.org/W2963820951","https://openalex.org/W2966138416","https://openalex.org/W2967515867","https://openalex.org/W2977530922","https://openalex.org/W2990152177","https://openalex.org/W2990503944","https://openalex.org/W3034572008","https://openalex.org/W3035400973","https://openalex.org/W3093108399","https://openalex.org/W3097009184","https://openalex.org/W3128064349","https://openalex.org/W3195852174","https://openalex.org/W3201864842","https://openalex.org/W3203640349","https://openalex.org/W4214564952","https://openalex.org/W4292793985","https://openalex.org/W4304080361","https://openalex.org/W4312347918","https://openalex.org/W4312652114","https://openalex.org/W4379652859","https://openalex.org/W4385489997","https://openalex.org/W4386065354","https://openalex.org/W4386071489","https://openalex.org/W4386076037","https://openalex.org/W4386076105","https://openalex.org/W4387968527","https://openalex.org/W4390871742","https://openalex.org/W4390872631","https://openalex.org/W4390872881","https://openalex.org/W4393148487","https://openalex.org/W4393154968","https://openalex.org/W4403758820","https://openalex.org/W4409368693"],"related_works":[],"abstract_inverted_index":{"Gait":[0],"recognition":[1],"has":[2],"emerged":[3],"as":[4],"a":[5,45,70,91,114,185],"promising":[6],"biometric":[7,200],"identification":[8,37],"method":[9],"due":[10],"to":[11,50,77,98,197,203],"its":[12],"non-cooperative":[13],"and":[14,160,170,175],"nonintrusive":[15],"nature.":[16],"However,":[17],"existing":[18],"methods":[19],"often":[20],"exhibit":[21],"limitations":[22,53],"in":[23,38,178],"effectively":[24],"modeling":[25,56,136,189],"the":[26,122,168,173,190],"temporal":[27,57,134,180,191],"dynamics":[28,58,135,192],"of":[29,59,65,153,172,193],"human":[30,194],"gait,":[31,195],"which":[32],"are":[33,67],"crucial":[34],"for":[35],"robust":[36,199],"real-world":[39,204],"scenarios.":[40],"This":[41,108,182],"paper":[42],"introduces":[43],"TDGait,":[44],"novel":[46],"network":[47],"specifically":[48],"designed":[49],"address":[51],"these":[52],"by":[54],"explicitly":[55],"gait":[60],"patterns.":[61],"The":[62],"core":[63],"innovations":[64],"TDGait":[66,125],"twofold:":[68],"1)":[69],"Temporal":[71],"Difference":[72],"Module":[73],"(TDM)":[74],"is":[75,96],"proposed":[76],"efficiently":[78],"capture":[79],"frame-to-frame":[80],"variations,":[81],"focusing":[82],"on":[83,143,155,158,162,188],"dynamic":[84,119],"changes":[85],"rather":[86],"than":[87],"static":[88],"appearance.":[89],"2)":[90],"Residual":[92],"Dynamic":[93],"Block":[94],"(RDB)":[95],"introduced":[97],"seamlessly":[99],"integrate":[100],"TDM":[101,138,174],"without":[102],"impairing":[103],"spatial":[104,111,128],"feature":[105,129],"learning":[106,112],"capacity.":[107],"block":[109],"preserves":[110],"via":[113],"residual":[115],"pathway":[116],"while":[117],"enhancing":[118],"information":[120],"through":[121],"embedded":[123],"TDM.":[124],"hierarchically":[126],"combines":[127],"extraction":[130],"with":[131],"this":[132],"explicit":[133],"using":[137],"within":[139],"RDBs.":[140],"Comprehensive":[141],"experiments":[142],"three":[144],"benchmark":[145],"datasets":[146],"demonstrate":[147],"state-of-the-art":[148],"performance,":[149],"achieving":[150],"Rank-1":[151],"accuracies":[152],"83.38%":[154],"SUSTech1K,":[156],"72.5%":[157],"Gait3D,":[159],"79.1%":[161],"GREW.":[163],"Extensive":[164],"ablation":[165],"studies":[166],"validate":[167],"effectiveness":[169],"efficiency":[171],"RDB":[176],"components":[177],"capturing":[179],"dynamics.":[181],"work":[183],"provides":[184],"new":[186],"perspective":[187],"contributing":[196],"more":[198],"systems":[201],"resilient":[202],"variations.":[205]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}