{"id":"https://openalex.org/W3196106775","doi":"https://doi.org/10.1109/iccta40200.2016.9512941","title":"Advanced Neural Network Gait Recognition System with Dynamic Thresholding","display_name":"Advanced Neural Network Gait Recognition System with Dynamic Thresholding","publication_year":2016,"publication_date":"2016-10-25","ids":{"openalex":"https://openalex.org/W3196106775","doi":"https://doi.org/10.1109/iccta40200.2016.9512941","mag":"3196106775"},"language":"en","primary_location":{"id":"doi:10.1109/iccta40200.2016.9512941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9512941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","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/A5061817898","display_name":"Ahmed Refaat Hawas","orcid":null},"institutions":[{"id":"https://openalex.org/I21376657","display_name":"Tanta University","ror":"https://ror.org/016jp5b92","country_code":"EG","type":"education","lineage":["https://openalex.org/I21376657"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Ahmed R. Hawas","raw_affiliation_strings":["Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt"],"affiliations":[{"raw_affiliation_string":"Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt","institution_ids":["https://openalex.org/I21376657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112707156","display_name":"Mustafa M. Abd-Elnaby","orcid":null},"institutions":[{"id":"https://openalex.org/I21376657","display_name":"Tanta University","ror":"https://ror.org/016jp5b92","country_code":"EG","type":"education","lineage":["https://openalex.org/I21376657"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mustafa M. Abd-Elnaby","raw_affiliation_strings":["Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt"],"affiliations":[{"raw_affiliation_string":"Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt","institution_ids":["https://openalex.org/I21376657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112750172","display_name":"Heba A. El-Khobby","orcid":null},"institutions":[{"id":"https://openalex.org/I21376657","display_name":"Tanta University","ror":"https://ror.org/016jp5b92","country_code":"EG","type":"education","lineage":["https://openalex.org/I21376657"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Heba El-Khobby","raw_affiliation_strings":["Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt"],"affiliations":[{"raw_affiliation_string":"Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt","institution_ids":["https://openalex.org/I21376657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080762590","display_name":"Fathi E. Abd El\u2010Samie","orcid":"https://orcid.org/0000-0001-8749-9518"},"institutions":[{"id":"https://openalex.org/I63601056","display_name":"Menoufia University","ror":"https://ror.org/05sjrb944","country_code":"EG","type":"education","lineage":["https://openalex.org/I63601056"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Fathi E. Abd El-Samie","raw_affiliation_strings":["Electronics and Electrical Communication Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt"],"affiliations":[{"raw_affiliation_string":"Electronics and Electrical Communication Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt","institution_ids":["https://openalex.org/I63601056"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061817898"],"corresponding_institution_ids":["https://openalex.org/I21376657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27436568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"62","last_page":"67"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9886999726295471,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7695348262786865},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7673323154449463},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.7669358849525452},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.6450050473213196},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6324038505554199},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6176409721374512},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5825662016868591},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"Background subtraction","score":0.5796425342559814},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.555967390537262},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5486944913864136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.52326500415802},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4834238290786743},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.42657190561294556},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42130908370018005},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4130135178565979},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16985252499580383},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.09629800915718079}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7695348262786865},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7673323154449463},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7669358849525452},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.6450050473213196},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6324038505554199},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6176409721374512},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5825662016868591},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.5796425342559814},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.555967390537262},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5486944913864136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.52326500415802},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4834238290786743},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.42657190561294556},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42130908370018005},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4130135178565979},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16985252499580383},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.09629800915718079},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccta40200.2016.9512941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9512941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1498220216","https://openalex.org/W2031558089","https://openalex.org/W2058226207","https://openalex.org/W2062536306","https://openalex.org/W2083231156","https://openalex.org/W2100086166","https://openalex.org/W2104381957","https://openalex.org/W2115415549","https://openalex.org/W2126030799","https://openalex.org/W2133059825","https://openalex.org/W2137474370","https://openalex.org/W2145814825","https://openalex.org/W2149516292","https://openalex.org/W2152115238","https://openalex.org/W2161315665","https://openalex.org/W2161769930","https://openalex.org/W6680096826"],"related_works":["https://openalex.org/W1622964048","https://openalex.org/W30315714","https://openalex.org/W1965274140","https://openalex.org/W779885325","https://openalex.org/W28439884","https://openalex.org/W2112497652","https://openalex.org/W2104051139","https://openalex.org/W2189228877","https://openalex.org/W2168082005","https://openalex.org/W2767830864"],"abstract_inverted_index":{"Non-interactive":[0],"biometric":[1,34],"systems":[2,35],"have":[3],"gained":[4],"an":[5,93,109],"enormous":[6],"interest":[7],"among":[8],"computer":[9],"vision":[10],"researchers":[11],"as":[12,36,55],"they":[13,37,75],"provide":[14],"more":[15,50],"efficient":[16,51],"and":[17,22,27,62,159,176],"reliable":[18],"ways":[19],"of":[20,32],"identification":[21,87,161],"verification":[23],"from":[24,108],"distance.":[25],"Face":[26],"gait":[28,174],"recognition":[29,48,54,66,181],"are":[30],"types":[31],"non-interactive":[33],"do":[38],"not":[39],"need":[40],"user":[41],"cooperation":[42],"with":[43,59,92,102,132,141,164],"the":[44,73,119,123,136,142,148,154,160,165],"surveillance":[45,83],"system.":[46],"Gait":[47,65],"is":[49,100,116,126,139,152,162,170],"than":[52],"face":[53],"it":[56,177],"can":[57],"deal":[58],"low":[60],"resolution":[61],"brightness":[63],"images.":[64],"aims":[67],"to":[68],"recognize":[69],"people":[70],"based":[71],"on":[72],"way":[74],"walk.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80],"propose":[81],"a":[82,89,173,179],"system":[84,156,169],"for":[85,118],"human":[86],"using":[88,129,172],"neural":[90,98,137],"network":[91,99,138],"enhanced":[94],"back-propagation":[95],"algorithm.":[96],"This":[97],"fed":[101],"time-varying":[103],"1D":[104],"distance":[105,144],"signals":[106],"calculated":[107],"extracted":[110,143],"silhouette":[111],"sequence.":[112,121],"Initially,":[113],"background":[114,130],"modeling":[115],"performed":[117,163],"video":[120],"Subsequently,":[122],"moving":[124],"object":[125],"being":[127],"segmented":[128],"subtraction":[131],"dynamic":[133],"thresholding.":[134],"Then,":[135],"trained":[140],"signals.":[145],"Finally,":[146],"when":[147],"target":[149],"image":[150],"sequence":[151],"fed,":[153],"proposed":[155,168],"extracts":[157],"features":[158],"classifier.":[166],"The":[167],"evaluated":[171],"database":[175],"achieves":[178],"good":[180],"performance.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}