{"id":"https://openalex.org/W2130563197","doi":"https://doi.org/10.1109/cvpr.2014.239","title":"Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild","display_name":"Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2130563197","doi":"https://doi.org/10.1109/cvpr.2014.239","mag":"2130563197"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2014.239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2014.239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://eprints.nottingham.ac.uk/31436/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024224610","display_name":"Georgios Tzimiropoulos","orcid":"https://orcid.org/0000-0002-1803-5338"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Georgios Tzimiropoulos","raw_affiliation_strings":["Department of Computing, Imperial College, London, U.K","School of Computer Science University of Lincoln, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Imperial College, London, U.K","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"School of Computer Science University of Lincoln, U.K","institution_ids":[]}]},{"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/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]},{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB","NL"],"is_corresponding":false,"raw_author_name":"Maja Pantic","raw_affiliation_strings":["Department of Computing, Imperial College, London, U.K","University of Twente The Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Imperial College, London, U.K","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"University of Twente The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024224610"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":32.8583,"has_fulltext":false,"cited_by_count":221,"citation_normalized_percentile":{"value":0.99811301,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1851","last_page":"1858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"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":0.9998999834060669,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/face","display_name":"Face (sociological concept)","score":0.7105031609535217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6113157868385315},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5921629667282104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5760459899902344},{"id":"https://openalex.org/keywords/gauss","display_name":"Gauss","score":0.5464769601821899},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3708731532096863},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14358359575271606}],"concepts":[{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.7105031609535217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6113157868385315},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5921629667282104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5760459899902344},{"id":"https://openalex.org/C161794534","wikidata":"https://www.wikidata.org/wiki/Q177493","display_name":"Gauss","level":2,"score":0.5464769601821899},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3708731532096863},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14358359575271606},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1109/cvpr.2014.239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2014.239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/1d5d91e5-601b-45f9-b6be-43caf145cd06","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/1d5d91e5-601b-45f9-b6be-43caf145cd06","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Tzimiropoulos, G & Pantic, M 2014, Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild. in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014. IEEE, USA, pp. 1851-1858, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, Ohio, United States, 23/06/14. https://doi.org/10.1109/CVPR.2014.239","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.649.4171","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.4171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://ibug.doc.ic.ac.uk/media/uploads/documents/tzimiro_pantic_cvpr_2014.pdf","raw_type":"text"},{"id":"pmh:oai:eprints.nottingham.ac.uk:31436","is_oa":true,"landing_page_url":"http://eprints.nottingham.ac.uk/31436/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402013","display_name":"Nottingham ePrints (University of Nottingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I142263535","host_organization_name":"University of Nottingham","host_organization_lineage":["https://openalex.org/I142263535"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:nottingham-repository.worktribe.com:999950","is_oa":true,"landing_page_url":"https://nottingham-repository.worktribe.com/output/999950","pdf_url":null,"source":{"id":"https://openalex.org/S4306402483","display_name":"Repository@Nottingham (University of Nottingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I142263535","host_organization_name":"University of Nottingham","host_organization_lineage":["https://openalex.org/I142263535"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/64929","is_oa":false,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/64929","pdf_url":null,"source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},{"id":"pmh:oai:ris.utwente.nl:publications/1d5d91e5-601b-45f9-b6be-43caf145cd06","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:eprints.nottingham.ac.uk:31436","is_oa":true,"landing_page_url":"http://eprints.nottingham.ac.uk/31436/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402013","display_name":"Nottingham ePrints (University of Nottingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I142263535","host_organization_name":"University of Nottingham","host_organization_lineage":["https://openalex.org/I142263535"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W25944667","https://openalex.org/W1499329969","https://openalex.org/W1796263212","https://openalex.org/W2006902452","https://openalex.org/W2032558548","https://openalex.org/W2036868818","https://openalex.org/W2038952578","https://openalex.org/W2045965808","https://openalex.org/W2076017598","https://openalex.org/W2082308025","https://openalex.org/W2098597588","https://openalex.org/W2101812986","https://openalex.org/W2103876808","https://openalex.org/W2109434346","https://openalex.org/W2138406903","https://openalex.org/W2147259701","https://openalex.org/W2151103935","https://openalex.org/W2152826865","https://openalex.org/W2157285372","https://openalex.org/W2161969291","https://openalex.org/W2163998463","https://openalex.org/W2168356304","https://openalex.org/W6601051309","https://openalex.org/W6629740406","https://openalex.org/W6638488279","https://openalex.org/W6648088351","https://openalex.org/W6676302190","https://openalex.org/W6682225606"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Arguably,":[0],"Deformable":[1,180],"Part":[2,181],"Models":[3],"(DPMs)":[4],"are":[5,46,82],"one":[6],"of":[7],"the":[8,50,134,161,176,190,195],"most":[9,30],"prominent":[10],"approaches":[11],"for":[12,21,57,133,208],"face":[13],"alignment":[14],"with":[15,49,120],"impressive":[16],"results":[17,126],"being":[18],"recently":[19],"reported":[20],"both":[22],"controlled":[23],"lab":[24],"and":[25,60,87,97,192],"unconstrained":[26],"settings.":[27],"Fitting":[28],"in":[29,93,104,127,200],"DPM":[31],"methods":[32],"is":[33,64,75,211],"typically":[34],"formulated":[35],"as":[36],"a":[37,54,112,121,128,152,166,204],"two-step":[38],"process":[39],"during":[40,155,172],"which":[41,81,125],"discriminatively":[42],"trained":[43,114],"part":[44,79],"templates":[45,80],"first":[47],"correlated":[48],"image":[51],"to":[52,84,91,109],"yield":[53],"filter":[55,68,95],"response":[56],"each":[58],"landmark":[59],"then":[61,158],"shape":[62,123],"optimization":[63],"performed":[65],"over":[66],"these":[67],"responses.":[69],"This":[70],"process,":[71],"although":[72],"computationally":[73],"efficient,":[74],"based":[76],"on":[77,165],"fixed":[78],"assumed":[83],"be":[85,148],"independent,":[86],"has":[88],"been":[89],"shown":[90],"result":[92],"imperfect":[94],"responses":[96],"detection":[98],"ambiguities.":[99],"To":[100],"address":[101],"this":[102,105],"limitation,":[103],"paper,":[106],"we":[107,185],"propose":[108],"jointly":[110],"optimize":[111],"part-based,":[113],"in-the-wild,":[115],"flexible":[116],"appearance":[117],"model":[118,124,132,135,154],"along":[119],"global":[122],"joint":[129],"translational":[130],"motion":[131],"parts":[136],"via":[137],"Gauss-Newton":[138,179],"(GN)":[139],"optimization.":[140],"We":[141,174],"show":[142,193],"how":[143],"significant":[144],"computational":[145],"reductions":[146],"can":[147],"achieved":[149],"by":[150,203],"building":[151],"full":[153],"training":[156],"but":[157],"efficiently":[159],"optimizing":[160],"proposed":[162,177,196],"cost":[163],"function":[164],"sparse":[167],"grid":[168],"using":[169],"weighted":[170],"least-squares":[171],"fitting.":[173],"coin":[175],"formulation":[178],"Model":[182],"(GN-DPM).":[183],"Finally,":[184],"compare":[186],"its":[187],"performance":[188],"against":[189],"state-of-the-art":[191],"that":[194],"GN-DPM":[197],"outperforms":[198],"it,":[199],"some":[201],"cases,":[202],"large":[205],"margin.":[206],"Code":[207],"our":[209],"method":[210],"available":[212],"from":[213],"http://ibug.doc.ic.ac.uk/resources.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":30},{"year":2017,"cited_by_count":52},{"year":2016,"cited_by_count":46},{"year":2015,"cited_by_count":33},{"year":2014,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
