{"id":"https://openalex.org/W2566854650","doi":"https://doi.org/10.1109/dicta.2016.7797006","title":"An Efficient Energy Model for Human Gait Recognition","display_name":"An Efficient Energy Model for Human Gait Recognition","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2566854650","doi":"https://doi.org/10.1109/dicta.2016.7797006","mag":"2566854650"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2016.7797006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5054413694","display_name":"Mohammad H. Ghaeminia","orcid":"https://orcid.org/0000-0002-4822-6864"},"institutions":[{"id":"https://openalex.org/I67009956","display_name":"Iran University of Science and Technology","ror":"https://ror.org/01jw2p796","country_code":"IR","type":"education","lineage":["https://openalex.org/I67009956"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad H. Ghaeminia","raw_affiliation_strings":["School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran","institution_ids":["https://openalex.org/I67009956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026599426","display_name":"Ali Badiezadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I67009956","display_name":"Iran University of Science and Technology","ror":"https://ror.org/01jw2p796","country_code":"IR","type":"education","lineage":["https://openalex.org/I67009956"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Ali Badiezadeh","raw_affiliation_strings":["School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran","institution_ids":["https://openalex.org/I67009956"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050563980","display_name":"Shahriar B. Shokouhi","orcid":"https://orcid.org/0000-0001-6266-6607"},"institutions":[{"id":"https://openalex.org/I67009956","display_name":"Iran University of Science and Technology","ror":"https://ror.org/01jw2p796","country_code":"IR","type":"education","lineage":["https://openalex.org/I67009956"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Shahriar B. Shokouhi","raw_affiliation_strings":["School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran","institution_ids":["https://openalex.org/I67009956"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.175,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60114888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"1","last_page":"6"},"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":0.9872000217437744,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/overfitting","display_name":"Overfitting","score":0.8889191150665283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7561770677566528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7245820760726929},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7112679481506348},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5152761936187744},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4958284795284271},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.47809287905693054},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4777780771255493},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4286603033542633},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4191426634788513},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1427660882472992},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11297294497489929}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8889191150665283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561770677566528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7245820760726929},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7112679481506348},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5152761936187744},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4958284795284271},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.47809287905693054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4777780771255493},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4286603033542633},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4191426634788513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1427660882472992},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11297294497489929},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2016.7797006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1147511243","https://openalex.org/W1534763723","https://openalex.org/W1561782558","https://openalex.org/W1569947815","https://openalex.org/W2007060009","https://openalex.org/W2016327746","https://openalex.org/W2018480038","https://openalex.org/W2025085710","https://openalex.org/W2026800967","https://openalex.org/W2032266162","https://openalex.org/W2042576467","https://openalex.org/W2065515169","https://openalex.org/W2084796520","https://openalex.org/W2091451699","https://openalex.org/W2093238900","https://openalex.org/W2108992228","https://openalex.org/W2112547580","https://openalex.org/W2118036996","https://openalex.org/W2118435112","https://openalex.org/W2126680226","https://openalex.org/W2127964696","https://openalex.org/W2138350282","https://openalex.org/W2149516292","https://openalex.org/W2151458682","https://openalex.org/W2154624311","https://openalex.org/W2169845441","https://openalex.org/W2542803194","https://openalex.org/W6632082525","https://openalex.org/W6633663260","https://openalex.org/W6634103385"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W2386960251"],"abstract_inverted_index":{"Spatio-temporal":[0],"features":[1,93],"can":[2],"extract":[3,100],"style":[4],"of":[5,25,33,44,80,84,143],"walking":[6,82],"in":[7,148,161,169],"a":[8,38,55,105],"video":[9,35],"sequence":[10],"and":[11,113,166],"are":[12,47,118],"used":[13],"to":[14],"model":[15,24],"human's":[16,50],"motion":[17,51],"for":[18,49,59,111,131],"specified":[19],"recognition":[20,61,72],"tasks.":[21],"The":[22,133],"generic":[23],"spatio-temporal":[26,107],"feature":[27],"is":[28,62,94,109,129],"derived":[29],"from":[30,96],"spatial":[31],"filtering":[32,45,57,108,146],"the":[34,68,76,85,91,101,115,137,144,172],"followed":[36],"by":[37],"temporal":[39],"filtering.":[40],"Recently,":[41],"several":[42],"types":[43],"kernels":[46],"proposed":[48,145,157],"analysis.":[52],"However,":[53],"utilizing":[54],"suitable":[56],"approach":[58],"gait":[60,71,92,116,139,163],"still":[63],"an":[64],"open":[65],"problem.":[66],"On":[67],"other":[69],"hand,":[70],"algorithms":[73],"may":[74],"confront":[75],"overfitting":[77,123],"problem,":[78,124],"because":[79],"single":[81],"condition":[83],"training":[86],"set.":[87],"In":[88],"this":[89],"paper,":[90],"studied":[95],"energy":[97,103],"viewpoint.":[98],"To":[99,121],"efficient":[102],"model,":[104],"general":[106],"developed":[110],"modeling":[112],"then,":[114],"templates":[117],"generated":[119],"accordingly.":[120],"address":[122],"Random":[125],"Subspace":[126],"Method":[127],"(RSM)":[128],"employed":[130],"classification.":[132],"experimental":[134],"results":[135],"on":[136],"USF":[138],"database":[140],"verify":[141],"accuracy":[142],"scheme":[147],"combination":[149],"with":[150,171],"RSM.":[151],"Moreover,":[152],"we":[153],"demonstrate":[154],"that":[155],"our":[156],"method":[158],"works":[159],"well":[160],"most":[162],"challenging":[164],"conditions":[165],"has":[167],"superiority":[168],"comparison":[170],"related":[173],"methods.":[174]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
