{"id":"https://openalex.org/W2403846478","doi":"https://doi.org/10.1109/icassp.2016.7472046","title":"Multi-fold Gabor filter convolution descriptor for face recognition","display_name":"Multi-fold Gabor filter convolution descriptor for face recognition","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2403846478","doi":"https://doi.org/10.1109/icassp.2016.7472046","mag":"2403846478"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5083277795","display_name":"Cheng-Yaw Low","orcid":"https://orcid.org/0000-0002-6764-0614"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Cheng-Yaw Low","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051093782","display_name":"Andrew Beng Jin Teoh","orcid":"https://orcid.org/0000-0001-5063-9484"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Andrew Beng-Jin Teoh","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045891222","display_name":"Cong-Jie Ng","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cong-Jie Ng","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering Yonsei University, Seoul, SOUTH KOREA","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083277795"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":1.8371,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.90120328,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"2094","last_page":"2098"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998999834060669,"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/T10828","display_name":"Biometric Identification and Security","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11448","display_name":"Face recognition and analysis","score":0.9994000196456909,"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/gabor-filter","display_name":"Gabor filter","score":0.7727444171905518},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7706918716430664},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7153632044792175},{"id":"https://openalex.org/keywords/gabor-wavelet","display_name":"Gabor wavelet","score":0.6780969500541687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5797973275184631},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5230579972267151},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4805584251880646},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47822946310043335},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.46874526143074036},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.46400561928749084},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4172355532646179},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40976667404174805},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39257127046585083},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12307113409042358},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12230297923088074},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.10462561249732971},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.0784546434879303},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.07837170362472534}],"concepts":[{"id":"https://openalex.org/C2779883129","wikidata":"https://www.wikidata.org/wiki/Q2447890","display_name":"Gabor filter","level":3,"score":0.7727444171905518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7706918716430664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7153632044792175},{"id":"https://openalex.org/C136902061","wikidata":"https://www.wikidata.org/wiki/Q16981559","display_name":"Gabor wavelet","level":5,"score":0.6780969500541687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5797973275184631},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5230579972267151},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4805584251880646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47822946310043335},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.46874526143074036},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.46400561928749084},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4172355532646179},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40976667404174805},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39257127046585083},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12307113409042358},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12230297923088074},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.10462561249732971},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.0784546434879303},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.07837170362472534},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W200171906","https://openalex.org/W1484228140","https://openalex.org/W1502160187","https://openalex.org/W1616262590","https://openalex.org/W1975056068","https://openalex.org/W1985425861","https://openalex.org/W2006500012","https://openalex.org/W2033419168","https://openalex.org/W2047186200","https://openalex.org/W2054891869","https://openalex.org/W2075772568","https://openalex.org/W2102796633","https://openalex.org/W2108082645","https://openalex.org/W2109779985","https://openalex.org/W2114407479","https://openalex.org/W2117072267","https://openalex.org/W2131081720","https://openalex.org/W2138100172","https://openalex.org/W2152942156","https://openalex.org/W2158424441","https://openalex.org/W2165731615","https://openalex.org/W2994340921","https://openalex.org/W3102431071","https://openalex.org/W6608082318","https://openalex.org/W6630005630","https://openalex.org/W6635552349","https://openalex.org/W6669324528","https://openalex.org/W6676587708"],"related_works":["https://openalex.org/W1811332408","https://openalex.org/W2070838085","https://openalex.org/W2374740620","https://openalex.org/W2495360253","https://openalex.org/W37053142","https://openalex.org/W2518455383","https://openalex.org/W2066448268","https://openalex.org/W2559560477","https://openalex.org/W2009069257","https://openalex.org/W2228937159"],"abstract_inverted_index":{"The":[0,113,136],"standard":[1],"multi-scale,":[2],"multi-orientation":[3],"Gabor":[4,42,54,81,104],"filter":[5,37,43,71,82],"ensemble":[6,83],"(SGFE)":[7],"in":[8,17],"the":[9,66,79,102,109,121,144,148,152],"face":[10,154],"recognition":[11],"task":[12],"reposits":[13],"40":[14],"filters":[15,55],"localized":[16],"8":[18,87],"orientations":[19],"and":[20,26,100,133],"5":[21],"scales,":[22],"with":[23],"a":[24,33,47,116],"real":[25],"an":[27,91],"imaginary":[28],"constituent.":[29],"This":[30],"paper":[31,75],"devises":[32],"simple":[34],"means":[35],"of":[36,49,85,115,151],"diversification,":[38],"dubbed":[39],"as":[40],"multi-fold":[41],"convolution":[44],"(-FGFC),":[45],"where":[46],"set":[48],"pre-selected":[50],"filters,":[51],"e.g.,":[52],"single-scale":[53],"across":[56],"varying":[57],"orientations,":[58],"are":[59],"self-cross":[60],"convolved":[61],"by":[62],"folds":[63],"to":[64,98,108],"instantiate":[65],"offspring":[67],"filters.":[68,88],"To":[69],"facilitate":[70],"selection":[72],"for-FGFC,":[73],"this":[74],"summarizes":[76],"SGFE":[77],"into":[78],"condensed":[80],"(CGFE)":[84],"only":[86],"In":[89],"addition,":[90],"average":[92],"histogram":[93],"pooling":[94],"operator":[95],"is":[96,124],"proposed":[97],"downsample":[99],"regulate":[101],"demodulated":[103],"phase":[105],"features":[106],"prior":[107],"final":[110],"compression":[111],"stage.":[112],"performance":[114],"specific":[117],"M-FGFC":[118],"instance,":[119],"i.e.,":[120],"2-FGFC":[122,145],"descriptor,":[123],"investigated":[125],"on":[126,139],"FERET":[127,130,140],"I":[128,141],"(frontal),":[129],"II":[131],"(nonfrontal)":[132],"AR":[134],"datasets.":[135],"experimental":[137],"results":[138],"substantiates":[142],"that":[143],"descriptor":[146],"outperforms":[147],"leading":[149],"state":[150],"art":[153],"descriptors.":[155]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}