{"id":"https://openalex.org/W2399701427","doi":"https://doi.org/10.1109/icassp.2016.7472047","title":"DCT based region log-tiedrank covariance matrices for face recognition","display_name":"DCT based region log-tiedrank covariance matrices for face recognition","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2399701427","doi":"https://doi.org/10.1109/icassp.2016.7472047","mag":"2399701427"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472047","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/A5075631732","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":true,"raw_author_name":"Cong Jie Ng","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic 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":["Yonsei University, Seodaemun-gu, Seoul, KR"],"affiliations":[{"raw_affiliation_string":"Yonsei University, Seodaemun-gu, Seoul, KR","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","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":false,"raw_author_name":"Cheng Yaw Low","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic 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/A5075631732"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.835,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79789216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"24","issue":null,"first_page":"2099","last_page":"2103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.993399977684021,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.7898038625717163},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6307628750801086},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5995922684669495},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5682143568992615},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.555194079875946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5306039452552795},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5048635601997375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5028066635131836},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4999387264251709},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4875885844230652},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48209837079048157},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.4810955226421356},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.4305807948112488},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.41700947284698486},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.3848387897014618},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3648854196071625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17339986562728882},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1440832018852234}],"concepts":[{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.7898038625717163},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6307628750801086},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5995922684669495},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5682143568992615},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.555194079875946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5306039452552795},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5048635601997375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5028066635131836},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4999387264251709},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4875885844230652},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48209837079048157},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.4810955226421356},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.4305807948112488},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.41700947284698486},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.3848387897014618},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3648854196071625},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17339986562728882},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1440832018852234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472047","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":22,"referenced_works":["https://openalex.org/W1513618424","https://openalex.org/W1549083695","https://openalex.org/W1865351371","https://openalex.org/W1983496390","https://openalex.org/W1986964250","https://openalex.org/W1997011019","https://openalex.org/W2006367437","https://openalex.org/W2035611952","https://openalex.org/W2048192550","https://openalex.org/W2078483536","https://openalex.org/W2080033855","https://openalex.org/W2082721627","https://openalex.org/W2107105977","https://openalex.org/W2107871762","https://openalex.org/W2133848164","https://openalex.org/W2160761668","https://openalex.org/W2161437355","https://openalex.org/W2163808566","https://openalex.org/W2994340921","https://openalex.org/W3102503590","https://openalex.org/W4242660797","https://openalex.org/W6635552349"],"related_works":["https://openalex.org/W2040968278","https://openalex.org/W1974588588","https://openalex.org/W2572601863","https://openalex.org/W2118568436","https://openalex.org/W2018001152","https://openalex.org/W2109377650","https://openalex.org/W4390642134","https://openalex.org/W2344632425","https://openalex.org/W1987404909","https://openalex.org/W2097670344"],"abstract_inverted_index":{"Gabor-based":[0],"region":[1,127],"covariance":[2,86,130,154],"matrix":[3,155],"(GRCM)":[4],"has":[5],"been":[6],"demonstrated":[7],"as":[8,56,66],"a":[9],"promising":[10],"descriptor":[11],"for":[12,34,91,121],"face":[13],"recognition.":[14],"However,":[15],"GRCM":[16],"requires":[17],"large":[18],"number":[19],"of":[20,61,83],"filters":[21,29],"to":[22,49,75,146],"achieve":[23],"satisfactory":[24],"performance.":[25],"Furthermore,":[26],"complex-valued":[27,62],"Gabor":[28,63],"require":[30],"double":[31],"convolution":[32],"operations":[33],"each":[35],"filter":[36,57,102,107],"that":[37,95,118],"makes":[38],"the":[39,45,72,80,84,100,105,135,148,158],"computation":[40],"more":[41],"expensive.":[42],"To":[43],"alleviate":[44],"problem,":[46],"we":[47,142],"propose":[48,144],"adopt":[50],"real-valued":[51],"discrete":[52],"cosine":[53],"transform":[54,69],"(DCT)":[55],"bank":[58],"in":[59],"place":[60],"filter.":[64],"DCT":[65,101],"an":[67],"orthogonal":[68],"however":[70],"decorrelates":[71],"signal,":[73],"leads":[74],"most":[76],"energies":[77],"fall":[78],"into":[79],"diagonal":[81],"entries":[82],"constructed":[85],"matrix,":[87],"which":[88,132],"is":[89,119],"ill-formed":[90],"RCM.":[92],"We":[93],"demonstrate":[94],"applying":[96],"non-linear":[97],"operation":[98],"on":[99],"responses":[103,108],"ameliorates":[104],"decorrelated":[106],"effects.":[109],"Apart":[110],"from":[111],"that,":[112],"while":[113],"RCM":[114,126],"offers":[115],"spatial":[116],"information":[117],"useful":[120],"recognition":[122,136],"tasks,":[123],"overly":[124],"small":[125],"renders":[128],"poor":[129],"estimation,":[131],"can":[133],"affect":[134],"performance":[137,163],"drastically.":[138],"In":[139],"this":[140],"paper":[141],"also":[143],"Log-TiedRank":[145,160],"mitigate":[147],"potential":[149],"undersampling":[150],"effect":[151],"suffered":[152],"by":[153],"estimation.":[156],"From":[157],"experiments":[159],"shows":[161],"surprising":[162],"boost":[164],"over":[165],"AIRM":[166],"and":[167,175],"Log-Euclidean":[168],"metric":[169],"especially":[170],"when":[171],"both":[172],"gallery":[173],"set":[174,177],"probe":[176],"have":[178],"very":[179],"different":[180],"distributions.":[181]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}