{"id":"https://openalex.org/W2895780017","doi":"https://doi.org/10.1109/ijcnn.2018.8489195","title":"Eye Detection Using Ensemble of Weak Classifiers Based on Correlation Filter","display_name":"Eye Detection Using Ensemble of Weak Classifiers Based on Correlation Filter","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2895780017","doi":"https://doi.org/10.1109/ijcnn.2018.8489195","mag":"2895780017"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5001375692","display_name":"Wesley L. Passos","orcid":"https://orcid.org/0000-0002-8505-8265"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Wesley L. Passos","raw_affiliation_strings":["Centro Federal de Educa\u00e7\u00e3o Tecnol\u00f3gica Celso Suckow da Fonseca, CEP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro Federal de Educa\u00e7\u00e3o Tecnol\u00f3gica Celso Suckow da Fonseca, CEP, Brazil","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027798472","display_name":"Gabriel Ara\u00fajo","orcid":"https://orcid.org/0000-0002-0033-3265"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gabriel M. Araujo","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023068404","display_name":"Amaro A. de Lima","orcid":"https://orcid.org/0000-0001-5397-6531"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Amaro A. Lima","raw_affiliation_strings":["Centro Federal de Educa\u00e7\u00e3o Tecnol\u00f3gica Celso Suckow da Fonseca, CEP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro Federal de Educa\u00e7\u00e3o Tecnol\u00f3gica Celso Suckow da Fonseca, CEP, Brazil","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054440598","display_name":"Felipe M. L. Ribeiro","orcid":"https://orcid.org/0000-0002-3646-9500"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Felipe M.L. Ribeiro","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081219742","display_name":"Eduardo A. B. da Silva","orcid":"https://orcid.org/0000-0001-7755-6988"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo A.B. Silva","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro, Cx.P. 68504, Rio de Janeiro, RJ, Brazil","institution_ids":["https://openalex.org/I122140584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16264818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9871000051498413,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7596943974494934},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7352809309959412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6270352602005005},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5982532501220703},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5577998757362366},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5441582798957825},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4806003272533417},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.4427696764469147},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38928598165512085},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19986453652381897}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7596943974494934},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7352809309959412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6270352602005005},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5982532501220703},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5577998757362366},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5441582798957825},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4806003272533417},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.4427696764469147},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38928598165512085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19986453652381897},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1570448133","https://openalex.org/W1605688901","https://openalex.org/W1680392829","https://openalex.org/W1895380008","https://openalex.org/W1964988669","https://openalex.org/W1975647954","https://openalex.org/W1976948919","https://openalex.org/W2032558548","https://openalex.org/W2043033468","https://openalex.org/W2046399019","https://openalex.org/W2067584999","https://openalex.org/W2084120575","https://openalex.org/W2102256090","https://openalex.org/W2116277445","https://openalex.org/W2126909398","https://openalex.org/W2130961458","https://openalex.org/W2131081720","https://openalex.org/W2150753773","https://openalex.org/W2163852958","https://openalex.org/W2169837008","https://openalex.org/W2171033594","https://openalex.org/W2397023066","https://openalex.org/W2605757803","https://openalex.org/W2912934387","https://openalex.org/W3097096317","https://openalex.org/W4212883601","https://openalex.org/W6637386731","https://openalex.org/W6678836876","https://openalex.org/W6682546010","https://openalex.org/W6711844864"],"related_works":["https://openalex.org/W2915435096","https://openalex.org/W1957831838","https://openalex.org/W4243868241","https://openalex.org/W2973321216","https://openalex.org/W2121434426","https://openalex.org/W3024801493","https://openalex.org/W2905430408","https://openalex.org/W1976415306","https://openalex.org/W2913388591","https://openalex.org/W3007653948"],"abstract_inverted_index":{"This":[0,24],"work":[1,25],"proposes":[2],"a":[3,34,41,55,72,77,154],"novel":[4],"system":[5,88],"for":[6,110,125],"detecting":[7],"racial":[8],"landmarks":[9],"in":[10,71,107,153,167,178,184,195],"images":[11],"using":[12,191],"an":[13,102],"ensemble":[14,42],"of":[15,33,83,105,115,121,139,181,197],"correlation-based":[16],"filters":[17],"known":[18],"as":[19],"Inner":[20],"Product":[21],"Detector":[22],"(IPD).":[23],"has":[26],"three":[27],"main":[28],"contributions:":[29],"i)":[30],"the":[31,50,61,81,87,96,108,116,126,134,140,147,157,176,179,182],"usage":[32],"bootstrap":[35],"aggregating":[36],"algorithm":[37],"(bagging),":[38],"to":[39,79,164,175],"produce":[40],"classifier":[43],"with":[44,49],"higher":[45],"accuracy":[46,104,198],"when":[47],"compared":[48,174],"original":[51],"IPD":[52,63],"detector;":[53],"ii)":[54],"new":[56],"discriminant":[57],"function":[58],"based":[59],"on":[60,95,133],"highest":[62],"mean":[64],"value":[65],"calculated":[66],"from":[67],"samples":[68],"positively":[69],"classified":[70],"voting":[73],"scheme;":[74],"iii)":[75],"and":[76,98,119,123,129,199],"study":[78],"assess":[80],"influence":[82],"class":[84],"unbalance":[85],"over":[86],"performance.":[89],"The":[90],"proposed":[91,158],"method":[92,159],"was":[93],"evaluated":[94],"BioID":[97,109],"LFPW":[99,135],"datasets,":[100],"achieving":[101],"average":[103],"93.3%":[106],"both":[111],"eyes,":[112],"at":[113,137,149],"10%":[114,138],"interocular":[117,141],"distance,":[118],"accuracies":[120],"85.2%":[122],"81.6%":[124],"left":[127],"eye":[128,185],"right":[130],"eyes":[131,148],"respectively,":[132],"database,":[136],"distance.":[142],"Since":[143],"it":[144],"can":[145],"detect":[146],"approximately":[150],"70":[151],"FPS":[152],"Matlab":[155],"implementation,":[156],"is":[160],"also":[161],"fast":[162],"enough":[163],"be":[165],"used":[166],"real":[168],"time":[169],"applications.":[170],"These":[171],"results":[172],"were":[173],"ones":[177],"state":[180],"art":[183],"detection":[186],"-":[187,194],"which":[188],"include":[189],"methods":[190],"deep":[192],"learning":[193],"terms":[196],"computational":[200],"complexity.":[201]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
