{"id":"https://openalex.org/W3111781129","doi":"https://doi.org/10.1109/ipta50016.2020.9286671","title":"Efficient Deep Face Alignment with Explicit Statistical Shape Models in Convolutional Neural Networks","display_name":"Efficient Deep Face Alignment with Explicit Statistical Shape Models in Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-11-09","ids":{"openalex":"https://openalex.org/W3111781129","doi":"https://doi.org/10.1109/ipta50016.2020.9286671","mag":"3111781129"},"language":"en","primary_location":{"id":"doi:10.1109/ipta50016.2020.9286671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta50016.2020.9286671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5061663719","display_name":"Marcin Kopaczka","orcid":"https://orcid.org/0000-0002-3695-4696"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Marcin Kopaczka","raw_affiliation_strings":["Institute of Imaging and Computer Vision, RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Imaging and Computer Vision, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077459898","display_name":"Justus Schock","orcid":"https://orcid.org/0000-0003-0512-3053"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Justus Schock","raw_affiliation_strings":["Institute of Imaging and Computer Vision, RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Imaging and Computer Vision, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010118809","display_name":"Paul W. Kruse","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Paul Kruse","raw_affiliation_strings":["Institute of Imaging and Computer Vision, RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Imaging and Computer Vision, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064747056","display_name":"Dorit Merhof","orcid":"https://orcid.org/0000-0002-1672-2185"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dorit Merhof","raw_affiliation_strings":["Institute of Imaging and Computer Vision, RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Imaging and Computer Vision, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061663719"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14764401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":1.0,"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":1.0,"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/T10860","display_name":"Speech and Audio Processing","score":0.9883000254631042,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9869999885559082,"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":0.8511291742324829},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7479363083839417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6649547219276428},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.6610573530197144},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5713592767715454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5599422454833984},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5563200116157532},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.544111430644989},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.44644686579704285},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4221191108226776},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.41509634256362915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8511291742324829},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7479363083839417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6649547219276428},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.6610573530197144},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5713592767715454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5599422454833984},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5563200116157532},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.544111430644989},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.44644686579704285},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4221191108226776},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.41509634256362915},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta50016.2020.9286671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta50016.2020.9286671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1604643028","https://openalex.org/W2082308025","https://openalex.org/W2157285372","https://openalex.org/W2736728583","https://openalex.org/W2740020909","https://openalex.org/W2770121394","https://openalex.org/W2780034403","https://openalex.org/W2798730128","https://openalex.org/W2915352401","https://openalex.org/W2962819150","https://openalex.org/W2962890819","https://openalex.org/W2962925415","https://openalex.org/W2963789946","https://openalex.org/W2964309795","https://openalex.org/W3011150210","https://openalex.org/W4288558691","https://openalex.org/W6759845760"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2026924879","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W2052388267","https://openalex.org/W2950647290","https://openalex.org/W1968481813","https://openalex.org/W2620829895","https://openalex.org/W2356918560"],"abstract_inverted_index":{"Face":[0],"alignment":[1,71],"is":[2,11],"commonly":[3],"solved":[4],"by":[5,49],"utilizing":[6],"a":[7,36,45,51,66,125],"shape":[8,26,42],"representation":[9],"which":[10,54],"either":[12],"implicitly":[13],"learned":[14],"as":[15,23],"in":[16,24,61,65],"current":[17],"deep":[18,46,69],"architectures":[19],"or":[20],"explicitly":[21],"given":[22],"active":[25],"models":[27,59],"and":[28,98],"their":[29],"derivatives.":[30],"In":[31,88],"this":[32],"work,":[33],"we":[34,95,118],"present":[35],"method":[37],"for":[38,101],"integrating":[39],"explicit":[40],"statistical":[41,57],"priors":[43],"into":[44],"convolutional":[47],"network":[48],"introducing":[50,91],"novel":[52],"layer":[53],"allows":[55],"using":[56],"PCA-based":[58],"directly":[60],"the":[62,92,107,114,128],"network,":[63],"resulting":[64],"highly":[67],"efficient":[68],"face":[70],"architecture":[72],"capable":[73],"of":[74,81,109,127,130],"performing":[75],"landmark":[76],"detection":[77],"with":[78],"several":[79],"hundreds":[80],"frames":[82],"per":[83],"second":[84],"on":[85,113],"consumer":[86],"hardware.":[87],"addition":[89],"to":[90],"core":[93],"algorithm,":[94],"also":[96],"introduce":[97],"discuss":[99],"extensions":[100],"improving":[102],"fitting":[103],"precision.":[104],"We":[105],"validate":[106],"performance":[108],"all":[110],"methods":[111],"quantitatively":[112],"300W":[115],"dataset,":[116],"where":[117],"achieve":[119],"state-of-the-art":[120],"precision":[121],"while":[122],"requiring":[123],"only":[124],"fraction":[126],"runtime":[129],"most":[131],"competing":[132],"methods.":[133]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}