{"id":"https://openalex.org/W2035049485","doi":"https://doi.org/10.1109/fg.2011.5771334","title":"Kernel spectral regression of perceived age from hybrid facial features","display_name":"Kernel spectral regression of perceived age from hybrid facial features","publication_year":2011,"publication_date":"2011-03-01","ids":{"openalex":"https://openalex.org/W2035049485","doi":"https://doi.org/10.1109/fg.2011.5771334","mag":"2035049485"},"language":"en","primary_location":{"id":"doi:10.1109/fg.2011.5771334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2011.5771334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Face and Gesture 2011","raw_type":"proceedings-article"},"type":"conference-paper","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/A5056226189","display_name":"Khoa Luu","orcid":"https://orcid.org/0000-0003-2104-0901"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Khoa Luu","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101511820","display_name":"Tien D. Bui","orcid":"https://orcid.org/0000-0001-6005-4375"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tien Dai Bui","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059479627","display_name":"Ching Y. Suen","orcid":"https://orcid.org/0000-0003-1209-7631"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ching Y. Suen","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Concordia University, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9991000294685364,"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/T11448","display_name":"Face recognition and analysis","score":0.9991000294685364,"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/T11094","display_name":"Face Recognition and Perception","score":0.9506000280380249,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13169","display_name":"Consumer Perception and Purchasing Behavior","score":0.9506000280380249,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5489332675933838},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5357503294944763},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239101648330688},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.48210859298706055},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47687608003616333},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3423752784729004},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29849815368652344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5489332675933838},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5357503294944763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239101648330688},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.48210859298706055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47687608003616333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3423752784729004},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29849815368652344},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fg.2011.5771334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2011.5771334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Face and Gesture 2011","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1513333765","https://openalex.org/W1545641654","https://openalex.org/W1556392566","https://openalex.org/W1967461346","https://openalex.org/W1983079504","https://openalex.org/W2056114654","https://openalex.org/W2056783896","https://openalex.org/W2061086108","https://openalex.org/W2061759159","https://openalex.org/W2073351001","https://openalex.org/W2102331633","https://openalex.org/W2103077782","https://openalex.org/W2106002835","https://openalex.org/W2106488920","https://openalex.org/W2107654046","https://openalex.org/W2121939926","https://openalex.org/W2131081720","https://openalex.org/W2134113392","https://openalex.org/W2137997339","https://openalex.org/W2138173495","https://openalex.org/W2149194912","https://openalex.org/W2149336145","https://openalex.org/W2151451863","https://openalex.org/W2156142937","https://openalex.org/W2158162711","https://openalex.org/W2163352848","https://openalex.org/W2163626514","https://openalex.org/W2164034100","https://openalex.org/W2164715565","https://openalex.org/W2169628874","https://openalex.org/W2172803778","https://openalex.org/W2534023444","https://openalex.org/W2537685535","https://openalex.org/W6632636493","https://openalex.org/W6675450563","https://openalex.org/W6675602974","https://openalex.org/W6680698592","https://openalex.org/W6682634897","https://openalex.org/W6684781068","https://openalex.org/W6728601258","https://openalex.org/W7002002311"],"related_works":["https://openalex.org/W1922851888","https://openalex.org/W2406961220","https://openalex.org/W3188962172","https://openalex.org/W1493568480","https://openalex.org/W2772917594","https://openalex.org/W2046260256","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"an":[3],"advanced":[4],"age-determination":[5,125],"technique":[6],"using":[7,85],"hybrid":[8,49],"facial":[9,36,50],"features":[10,51,58,81,113],"and":[11,34,56,145,170],"Kernel":[12,95],"Spectral":[13,96],"Regression,":[14],"a":[15],"nonlinear":[16],"dimensionality":[17],"reduction":[18],"method.":[19],"In":[20],"the":[21,24,48,62,75,79,86],"preprocessing":[22],"stage,":[23],"logarithmic":[25],"nonsubsampled":[26],"contourlet":[27],"transform":[28],"(NSCT)":[29],"is":[30,98,127],"conducted":[31],"to":[32,40,91,100,116,148],"denoise":[33],"amplify":[35],"wrinkles":[37],"that":[38,52],"help":[39],"distinguish":[41],"young":[42],"faces":[43,118],"from":[44,61],"elder":[45],"ones.":[46],"Then":[47],"combine":[53],"both":[54],"local":[55,76],"holistic":[57,80],"are":[59,72,82,114],"extracted":[60,83],"preprocessed":[63],"images.":[64],"Our":[65],"novel":[66],"Uniform":[67],"Local":[68],"Ternary":[69],"Patterns":[70],"(ULTP)":[71],"used":[73,99,115],"as":[74],"features.":[77],"Meanwhile":[78],"by":[84],"Active":[87],"Appearance":[88],"Model":[89],"(AAM)":[90],"encode":[92],"each":[93,131],"face.":[94],"Regression":[97],"minimize":[101],"inter-class":[102],"distances":[103,107],"while":[104],"maximizing":[105],"intra-class":[106],"of":[108,167],"feature":[109],"sets.":[110],"These":[111],"reduced":[112],"classify":[117],"into":[119],"two":[120],"age":[121,132],"groups":[122],"(age-classification).":[123],"An":[124],"function":[126],"then":[128],"constructed":[129],"for":[130,140],"group":[133],"in":[134,156,174],"accordance":[135],"with":[136],"physiological":[137],"growth":[138],"periods":[139],"humans":[141],"-":[142],"pre-adult":[143],"(youth)":[144],"adult.":[146],"Compared":[147],"published":[149],"results,":[150],"this":[151],"method":[152],"yields":[153],"promising":[154],"results":[155],"overall":[157],"mean":[158,162],"absolute":[159,163],"error":[160,164],"(MAE),":[161],"per":[165],"decade":[166],"life":[168],"(MAE/D),":[169],"cumulative":[171],"match":[172],"score":[173],"various":[175],"face":[176],"aging":[177],"corpuses.":[178]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
