{"id":"https://openalex.org/W2146342856","doi":"https://doi.org/10.1109/ijcnn.2003.1224070","title":"Incorporating invariants in mahalanobis distance based classifiers: application to face recognition","display_name":"Incorporating invariants in mahalanobis distance based classifiers: application to face recognition","publication_year":2004,"publication_date":"2004-06-22","ids":{"openalex":"https://openalex.org/W2146342856","doi":"https://doi.org/10.1109/ijcnn.2003.1224070","mag":"2146342856"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2003.1224070","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1224070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","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/A5065570794","display_name":"Andrew M. Fraser","orcid":"https://orcid.org/0000-0003-3065-3128"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]},{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"A.M. Fraser","raw_affiliation_strings":["Los Alamos National Laboratory, Portland State University, USA","Los Alamos National Labs, NM, USA"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Portland State University, USA","institution_ids":["https://openalex.org/I1343871089","https://openalex.org/I126345244"]},{"raw_affiliation_string":"Los Alamos National Labs, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041470785","display_name":"Nicolas Hengartner","orcid":"https://orcid.org/0000-0002-4157-134X"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"N.W. Hengartner","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA","Los Alamos National Labs, NM, USA"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]},{"raw_affiliation_string":"Los Alamos National Labs, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080405949","display_name":"Kevin R. Vixie","orcid":"https://orcid.org/0000-0001-8384-3545"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K.R. Vixie","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA","Los Alamos National Labs, NM, USA"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]},{"raw_affiliation_string":"Los Alamos National Labs, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015244709","display_name":"Brendt Wohlberg","orcid":"https://orcid.org/0000-0002-4767-1843"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"B.E. Wohlberg","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA","Los Alamos National Labs, NM, USA"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]},{"raw_affiliation_string":"Los Alamos National Labs, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065570794"],"corresponding_institution_ids":["https://openalex.org/I126345244","https://openalex.org/I1343871089"],"apc_list":null,"apc_paid":null,"fwci":1.1043,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.80391861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"4","issue":null,"first_page":"3118","last_page":"3123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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/T10320","display_name":"Neural Networks and Applications","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9850999712944031,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.8327467441558838},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7109062671661377},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7003985643386841},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6545191407203674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6457958221435547},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4770078659057617},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4269421398639679},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.42523714900016785},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.418901652097702},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40879595279693604},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.258328378200531},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2066086232662201}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.8327467441558838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7109062671661377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7003985643386841},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6545191407203674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6457958221435547},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4770078659057617},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4269421398639679},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.42523714900016785},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.418901652097702},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40879595279693604},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.258328378200531},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2066086232662201},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2003.1224070","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1224070","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.146.2404","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.2404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://math.lanl.gov/~brendt/Publications/Docs/fraser-2003-invariants.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320337547","display_name":"Laboratory Directed Research and Development","ror":"https://ror.org/01e41cf67"},{"id":"https://openalex.org/F4320338304","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1600737329","https://openalex.org/W1761337995","https://openalex.org/W2033419168","https://openalex.org/W2050851555","https://openalex.org/W2098693229","https://openalex.org/W2123921160","https://openalex.org/W2139085189","https://openalex.org/W2160059488","https://openalex.org/W4234674466","https://openalex.org/W6635925994"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W1995039490","https://openalex.org/W2182646186","https://openalex.org/W2116423617","https://openalex.org/W2003054897","https://openalex.org/W2077878713","https://openalex.org/W2370292837","https://openalex.org/W4210360969","https://openalex.org/W2386644757","https://openalex.org/W2114428029"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,15,80,111,153,171,191,201,210],"technique":[3],"for":[4],"combining":[5],"prior":[6,160],"knowledge":[7],"about":[8],"transformations":[9,85],"that":[10,67,204],"should":[11],"be":[12],"ignored":[13],"with":[14,89],"covariance":[16],"matrix":[17],"estimated":[18],"from":[19,60,178],"training":[20,64,179],"data":[21,65,180],"to":[22,58,75,91,98,184,198],"make":[23],"an":[24,108],"improved":[25],"Mahalanobis":[26],"distance":[27],"classifier.":[28],"Modern":[29],"classification":[30,102],"problems":[31],"often":[32,56,126],"involve":[33],"objects":[34,95],"represented":[35],"by":[36,159,200],"high-dimensional":[37],"vectors":[38],"or":[39,45,86],"images":[40],"(for":[41,103],"example,":[42,104],"sampled":[43],"speech":[44],"human":[46],"faces).":[47],"The":[48],"complex":[49],"statistical":[50,155,176],"structure":[51],"of":[52,107,110,119,132,142,152,162,205],"these":[53],"representations":[54,88],"is":[55],"difficult":[57],"infer":[59],"the":[61,87,93,100,117,120,130,133,140,143,150,206],"relatively":[62,127],"limited":[63],"sets":[66],"are":[68,96,125,136],"available":[69,79],"in":[70,129,139,209],"practice.":[71],"Thus,":[72],"we":[73,168],"wish":[74],"efficiently":[76],"utilize":[77],"any":[78],"priori":[81],"information,":[82],"such":[83],"as":[84],"respect":[90],"which":[92,124,174],"associated":[94],"known":[97,185],"retain":[99],"same":[101],"spatial":[105],"shifts":[106],"image":[109],"handwritten":[112],"digit":[113],"do":[114],"not":[115],"alter":[116],"identity":[118],"digit).":[121],"These":[122],"transformations,":[123],"simple":[128],"space":[131,141],"underlying":[134],"objects,":[135],"usually":[137],"nonlinear":[138],"object":[144],"representation,":[145],"making":[146],"their":[147],"inclusion":[148],"within":[149],"framework":[151],"standard":[154],"classifier":[156,173],"difficult.":[157],"Motivated":[158],"work":[161],"Simard":[163],"et":[164],"al.":[165],"(1998;":[166],"2000),":[167],"have":[169],"constructed":[170],"new":[172],"combines":[175],"information":[177],"and":[181],"linear":[182],"approximations":[183],"invariance":[186],"transformations.":[187],"When":[188],"tested":[189],"on":[190],"face":[192],"recognition":[193],"task,":[194],"performance":[195],"was":[196],"found":[197],"exceed":[199],"significant":[202],"margin":[203],"best":[207],"algorithm":[208],"reference":[211],"software":[212],"distribution.":[213]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
