{"id":"https://openalex.org/W2589047175","doi":"https://doi.org/10.1109/mfi.2016.7849516","title":"Bayesian score level fusion for facial recognition","display_name":"Bayesian score level fusion for facial recognition","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2589047175","doi":"https://doi.org/10.1109/mfi.2016.7849516","mag":"2589047175"},"language":"en","primary_location":{"id":"doi:10.1109/mfi.2016.7849516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2016.7849516","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 Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5031354877","display_name":"Marco F. Huber","orcid":"https://orcid.org/0000-0002-8250-2092"},"institutions":[{"id":"https://openalex.org/I4210087769","display_name":"Software AG (Italy)","ror":"https://ror.org/003jrsn97","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210087769"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Marco F. Huber","raw_affiliation_strings":["USU Software AG, R\u00fcppurrer Str. 1, Karlsruhe"],"affiliations":[{"raw_affiliation_string":"USU Software AG, R\u00fcppurrer Str. 1, Karlsruhe","institution_ids":["https://openalex.org/I4210087769"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068012077","display_name":"Andreas Merentitis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Merentitis","raw_affiliation_strings":["The authors were with AGT International as this work was conducted","Zalando AG, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"The authors were with AGT International as this work was conducted","institution_ids":[]},{"raw_affiliation_string":"Zalando AG, Berlin, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059519968","display_name":"Roel Heremans","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roel Heremans","raw_affiliation_strings":["AGT International, Darmstadt, Germany"],"affiliations":[{"raw_affiliation_string":"AGT International, Darmstadt, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056672229","display_name":"Maria E. Niessen","orcid":null},"institutions":[{"id":"https://openalex.org/I1319473763","display_name":"Volkswagen Group (Germany)","ror":"https://ror.org/01f3bhg26","country_code":"DE","type":"company","lineage":["https://openalex.org/I1319473763"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Maria Niessen","raw_affiliation_strings":["The authors were with AGT International as this work was conducted","Volkswagen AG, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"The authors were with AGT International as this work was conducted","institution_ids":[]},{"raw_affiliation_string":"Volkswagen AG, Munich, Germany","institution_ids":["https://openalex.org/I1319473763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023275599","display_name":"Christian Debes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christian Debes","raw_affiliation_strings":["AGT International, Darmstadt, Germany"],"affiliations":[{"raw_affiliation_string":"AGT International, Darmstadt, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065929974","display_name":"Nikolaos Frangiadakis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nikolaos Frangiadakis","raw_affiliation_strings":["AGT International, Darmstadt, Germany"],"affiliations":[{"raw_affiliation_string":"AGT International, Darmstadt, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5031354877"],"corresponding_institution_ids":["https://openalex.org/I4210087769"],"apc_list":null,"apc_paid":null,"fwci":0.334,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69035192,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980999827384949,"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":0.9980999827384949,"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":0.9980000257492065,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9832000136375427,"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/computer-science","display_name":"Computer science","score":0.7132276296615601},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6908760666847229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6556717157363892},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6284822225570679},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5508560538291931},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5402959585189819},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48375844955444336},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.43651437759399414},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.41339319944381714},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.41001346707344055},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3673511743545532},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3656958043575287},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36265039443969727},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13825500011444092},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1284165382385254}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132276296615601},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6908760666847229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6556717157363892},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6284822225570679},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5508560538291931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5402959585189819},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48375844955444336},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.43651437759399414},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.41339319944381714},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.41001346707344055},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3673511743545532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3656958043575287},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36265039443969727},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13825500011444092},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1284165382385254},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mfi.2016.7849516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2016.7849516","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 Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1519435346","https://openalex.org/W1545672070","https://openalex.org/W1576817603","https://openalex.org/W1835702218","https://openalex.org/W1920942950","https://openalex.org/W2024677982","https://openalex.org/W2052713505","https://openalex.org/W2069270009","https://openalex.org/W2078136378","https://openalex.org/W2080565523","https://openalex.org/W2119072456","https://openalex.org/W2120433720","https://openalex.org/W2136885397","https://openalex.org/W2139340096","https://openalex.org/W2140785063","https://openalex.org/W2140959843","https://openalex.org/W2153096970","https://openalex.org/W2161746511","https://openalex.org/W2164745018","https://openalex.org/W2168175751","https://openalex.org/W2168227362","https://openalex.org/W2169513470","https://openalex.org/W4212863985","https://openalex.org/W6631147972","https://openalex.org/W6632794513","https://openalex.org/W6640550848","https://openalex.org/W6683780590","https://openalex.org/W6684452406"],"related_works":["https://openalex.org/W1971268144","https://openalex.org/W4390606538","https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W2010643158","https://openalex.org/W3049691116","https://openalex.org/W2106867672","https://openalex.org/W4310268968","https://openalex.org/W3081214562"],"abstract_inverted_index":{"Partial":[0],"occlusions,":[1],"changing":[2],"lighting":[3],"conditions,":[4],"or":[5],"rapid":[6],"motion":[7],"of":[8,17,42,51,86,101],"persons":[9],"are":[10],"some":[11],"reasons":[12],"why":[13],"the":[14,35,40,49,84,97],"recognition":[15,20,81],"rate":[16],"a":[18,43,58],"facial":[19],"(FR)":[21],"system":[22,45],"can":[23],"be":[24],"very":[25],"low.":[26],"One":[27],"approach":[28,93],"that":[29,69],"has":[30],"gained":[31],"increased":[32],"interest":[33],"in":[34,111],"recent":[36],"years":[37],"for":[38,79],"compensating":[39],"limitations":[41],"single":[44],"is":[46,67,77,94],"to":[47],"fuse":[48],"detections":[50],"multiple":[52],"FR":[53,103],"systems.":[54],"In":[55],"this":[56],"paper,":[57],"novel":[59],"fusion":[60],"algorithm":[61],"operating":[62],"on":[63],"match":[64],"score":[65],"level":[66],"proposed":[68,92],"follows":[70],"Bayesian":[71],"inference":[72],"and":[73,82,105],"decision":[74],"theory.":[75],"It":[76],"designed":[78],"on-line":[80],"facilitates":[83],"incorporation":[85],"temporal":[87],"correlation":[88],"between":[89],"detections.":[90],"The":[91],"compared":[95],"against":[96],"state-of-the-art":[98],"by":[99],"means":[100],"standard":[102],"benchmarks":[104],"an":[106,112],"extensive":[107],"person":[108],"detection":[109],"experiment":[110],"office":[113],"environment.":[114]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
