{"id":"https://openalex.org/W2155799207","doi":"https://doi.org/10.1109/afgr.2004.1301515","title":"Particle filtering with factorized likelihoods for tracking facial features","display_name":"Particle filtering with factorized likelihoods for tracking facial features","publication_year":2004,"publication_date":"2004-06-10","ids":{"openalex":"https://openalex.org/W2155799207","doi":"https://doi.org/10.1109/afgr.2004.1301515","mag":"2155799207"},"language":"en","primary_location":{"id":"doi:10.1109/afgr.2004.1301515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/afgr.2004.1301515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.","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/A5031205865","display_name":"Ioannis Patras","orcid":"https://orcid.org/0000-0003-3913-4738"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"I. Patras","raw_affiliation_strings":["Knowledge and Data Engineering Group, Delft University of Technnology, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"Knowledge and Data Engineering Group, Delft University of Technnology, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016033078","display_name":"Maja Panti\u0107","orcid":"https://orcid.org/0000-0002-3620-5986"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"M. Pantic","raw_affiliation_strings":["Knowledge and Data Engineering Group, Delft University of Technnology, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"Knowledge and Data Engineering Group, Delft University of Technnology, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031205865"],"corresponding_institution_ids":["https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":4.6898,"has_fulltext":false,"cited_by_count":102,"citation_normalized_percentile":{"value":0.95509005,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"97","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9782000184059143,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9487000107765198,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7589292526245117},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6496267914772034},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6386204957962036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.602581262588501},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6007683277130127},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5666570067405701},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5341370105743408},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.502251148223877},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.48929378390312195},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34844356775283813},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3303283751010895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2879277169704437},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2128150761127472},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.1882873773574829},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11164039373397827}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7589292526245117},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6496267914772034},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6386204957962036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602581262588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6007683277130127},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5666570067405701},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5341370105743408},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.502251148223877},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.48929378390312195},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34844356775283813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3303283751010895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2879277169704437},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2128150761127472},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.1882873773574829},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11164039373397827},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/afgr.2004.1301515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/afgr.2004.1301515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.4.1491","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.1491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.kbs.twi.tudelft.nl/People/Staff/M.Pantic/PatrasPanticFG04-final.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1513768190","https://openalex.org/W1552529795","https://openalex.org/W1595217348","https://openalex.org/W1899675034","https://openalex.org/W1910898529","https://openalex.org/W2105297815","https://openalex.org/W2116023383","https://openalex.org/W2131432112","https://openalex.org/W2131598171","https://openalex.org/W2133952369","https://openalex.org/W2149020252","https://openalex.org/W2161406034","https://openalex.org/W2966436128","https://openalex.org/W4244486013","https://openalex.org/W6630587866","https://openalex.org/W6635397906","https://openalex.org/W6640096618","https://openalex.org/W6680164740"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W2085259108","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W3123087812","https://openalex.org/W2063076820"],"abstract_inverted_index":{"In":[0,45],"the":[1,8,37,40,53,78,81,85,96,99,105,125],"recent":[2],"years":[3],"particle":[4,163],"filtering":[5,164],"has":[6],"been":[7],"dominant":[9],"paradigm":[10],"for":[11],"tracking":[12,130,159],"facial":[13,132],"and":[14,20,110,161],"body":[15],"features,":[16],"recognizing":[17],"temporal":[18],"events":[19],"reasoning":[21],"in":[22,59,145],"uncertainty.":[23],"A":[24],"major":[25],"problem":[26,51,126],"associated":[27],"with":[28],"it":[29],"is":[30,43,77,141],"that":[31,95,112,140,151,165],"its":[32],"performance":[33],"deteriorates":[34],"drastically":[35],"when":[36,52],"dimensionality":[38],"of":[39,61,80,84,87,101,127,131],"state":[41,54],"space":[42,55],"high.":[44],"this":[46,50],"paper,":[47],"we":[48],"address":[49],"can":[56,66,107,115],"be":[57,67,108,116],"partitioned":[58],"groups":[60,86,100],"random":[62,88,102],"variables":[63,103],"whose":[64],"likelihood":[65],"independently":[68],"evaluated.":[69],"We":[70,120,134,148],"introduce":[71],"a":[72,113,136],"novel":[73],"proposal":[74],"density,":[75],"which":[76],"product":[79],"marginal":[82],"posteriors":[83],"variables.":[89],"The":[90],"proposed":[91],"method":[92],"requires":[93],"only":[94],"interdependencies":[97],"between":[98],"(i.e.":[104],"priors)":[106],"evaluated":[109],"not":[111],"sample":[114],"drawn":[117],"from":[118],"them.":[119],"adapt":[121],"our":[122,152],"scheme":[123],"to":[124,143],"multiple":[128,156],"template-based":[129],"features.":[133],"propose":[135],"color-based":[137],"observation":[138],"model":[139],"invariant":[142],"changes":[144],"illumination":[146],"intensity.":[147],"experimentally":[149],"show":[150],"algorithm":[153],"clearly":[154],"outperforms":[155],"independent":[157],"template":[158],"schemes":[160],"auxiliary":[162],"utilizes":[166],"priors.":[167]},"counts_by_year":[{"year":2021,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":10}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}