{"id":"https://openalex.org/W3135865128","doi":null,"title":"Deep Active Shape Model for Face Alignment and Pose Estimation","display_name":"Deep Active Shape Model for Face Alignment and Pose Estimation","publication_year":2021,"publication_date":"2021-02-27","ids":{"openalex":"https://openalex.org/W3135865128","mag":"3135865128"},"language":"en","primary_location":{"id":"mag:3134413338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.00119v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2103.00119v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008504935","display_name":"Ali Pourramezan Fard","orcid":"https://orcid.org/0000-0002-3807-0798","observed_orcids":["https://orcid.org/0000-0002-3807-0798"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali Pourramezan Fard","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3807-0798","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033395928","display_name":"Hojjat Abdollahi","orcid":"https://orcid.org/0000-0002-0233-7737","observed_orcids":["https://orcid.org/0000-0002-0233-7737"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hojjat Abdollahi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0233-7737","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041948053","display_name":"Mohammad H. Mahoor","orcid":"https://orcid.org/0000-0001-8923-4660","observed_orcids":["https://orcid.org/0000-0001-8923-4660"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad H. Mahoor","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8923-4660","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/T11094","display_name":"Face Recognition and Perception","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/active-shape-model","display_name":"active shape model","score":0.8889999985694885},{"id":"https://openalex.org/keywords/facial-landmark-detection","display_name":"facial landmark detection","score":0.9850000143051147},{"id":"https://openalex.org/keywords/head-pose-estimation","display_name":"head pose estimation","score":0.9139999747276306},{"id":"https://openalex.org/keywords/lightweight-convolutional-neural-network","display_name":"lightweight convolutional neural network","score":0.8899999856948853},{"id":"https://openalex.org/keywords/multi-task-loss-function","display_name":"multi-task loss function","score":0.8090000152587891},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.7369999885559082},{"id":"https://openalex.org/keywords/parameter-efficiency","display_name":"parameter efficiency","score":0.4169999957084656}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.894041895866394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7994996905326843},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7844220995903015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7817877531051636},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6812570691108704},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6417848467826843},{"id":"https://openalex.org/C118317068","wikidata":"https://www.wikidata.org/wiki/Q2100760","display_name":"Point distribution model","level":2,"score":0.5894537568092346},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5496970415115356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5102570056915283},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4728905260562897},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4537045359611511},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.44649991393089294},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.429684579372406},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4155727028846741},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:3134413338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.00119v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"mag:3135865128","doi":null,"is_oa":false,"landing_page_url":"http://export.arxiv.org/pdf/2103.00119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"mag:3134413338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.00119v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W204612701","https://openalex.org/W1555415111","https://openalex.org/W1682276745","https://openalex.org/W1796263212","https://openalex.org/W1832881114","https://openalex.org/W1849277567","https://openalex.org/W1960706641","https://openalex.org/W1976948919","https://openalex.org/W1977821862","https://openalex.org/W1989702938","https://openalex.org/W1990937109","https://openalex.org/W1998294030","https://openalex.org/W1998808035","https://openalex.org/W2019177772","https://openalex.org/W2037019340","https://openalex.org/W2047508432","https://openalex.org/W2058961190","https://openalex.org/W2101866605","https://openalex.org/W2107887405","https://openalex.org/W2111372597","https://openalex.org/W2111440402","https://openalex.org/W2112695787","https://openalex.org/W2127862149","https://openalex.org/W2150753773","https://openalex.org/W2152826865","https://openalex.org/W2167955297","https://openalex.org/W2197128021","https://openalex.org/W2290180618","https://openalex.org/W2307770531","https://openalex.org/W2437557374","https://openalex.org/W2474575620","https://openalex.org/W2554268477","https://openalex.org/W2557728737","https://openalex.org/W2623892189","https://openalex.org/W2724103664","https://openalex.org/W2736671157","https://openalex.org/W2744972750","https://openalex.org/W2761173690","https://openalex.org/W2770121394","https://openalex.org/W2789228405","https://openalex.org/W2798730128","https://openalex.org/W2798898313","https://openalex.org/W2897558462","https://openalex.org/W2962925415","https://openalex.org/W2963163009","https://openalex.org/W2963566548","https://openalex.org/W2963789946","https://openalex.org/W2963993755","https://openalex.org/W2964014798","https://openalex.org/W2964309795"],"related_works":["https://openalex.org/W3160804301","https://openalex.org/W3134413338","https://openalex.org/W2122443056","https://openalex.org/W2805943005","https://openalex.org/W7067667927","https://openalex.org/W3139413313","https://openalex.org/W2990952853","https://openalex.org/W2963483939","https://openalex.org/W2724103664","https://openalex.org/W2964171387"],"abstract_inverted_index":{"Active":[0],"Shape":[1],"Model":[2],"(ASM)":[3],"is":[4,156,187],"a":[5,13,24,39,47,223],"statistical":[6],"model":[7],"of":[8,26,77,141,148,227],"object":[9,30],"shapes":[10],"that":[11,116,176],"represents":[12],"target":[14],"structure.":[15],"ASM":[16,53,66,181],"can":[17],"guide":[18,69],"machine":[19],"learning":[20,73,103],"algorithms":[21],"to":[22,67,233],"fit":[23],"set":[25],"points":[27,107,216],"representing":[28],"an":[29,34,210],"(e.g.,":[31],"face)":[32],"onto":[33],"image.":[35],"This":[36],"paper":[37],"presents":[38],"lightweight":[40],"Convolutional":[41],"Neural":[42],"Network":[43],"(CNN)":[44],"architecture":[45],"with":[46,153,189],"loss":[48,114,183],"function":[49,115],"being":[50],"assisted":[51,182],"by":[52,88,177],"for":[54,119,196,213],"face":[55,128,164,192,197],"alignment":[56,165,193],"and":[57,98,137,166,218,229],"estimating":[58,127],"head":[59],"pose":[60,167,198,219],"in":[61,112,162,191],"the":[62,70,74,78,84,89,95,100,104,139,146,179],"wild.":[63],"We":[64,109,144],"use":[65],"first":[68],"network":[71,101],"towards":[72,102],"smoother":[75],"distribution":[76],"facial":[79,121,214],"landmark":[80,106,122,215],"points.":[81],"Then,":[82],"during":[83],"training":[85],"process,":[86],"inspired":[87],"transfer":[90],"learning,":[91],"we":[92],"gradually":[93],"harden":[94],"regression":[96],"problem":[97],"lead":[99],"original":[105],"distribution.":[108],"define":[110],"multi-tasks":[111],"our":[113,149],"are":[117],"responsible":[118],"detecting":[120],"points,":[123],"as":[124,126],"well":[125],"pose.":[129],"Learning":[130],"multiple":[131],"correlated":[132],"tasks":[133],"simultaneously":[134],"builds":[135],"synergy":[136],"improves":[138],"performance":[140,147,186,212],"individual":[142],"tasks.":[143,169],"compare":[145],"proposed":[150,180,236],"CNN,":[151],"ASMNet":[152,185,200,208],"MobileNetV2":[154,190],"(which":[155],"about":[157],"2":[158],"times":[159],"bigger":[160],"ASMNet)":[161],"both":[163],"estimation":[168,220],"Experimental":[170],"results":[171],"on":[172],"challenging":[173],"datasets":[174],"show":[175],"using":[178],"function,":[184],"comparable":[188],"task.":[194],"Besides,":[195],"estimation,":[199],"performs":[201],"much":[202],"better":[203],"than":[204],"MobileNetV2.":[205],"Moreover,":[206],"overall":[207],"achieves":[209],"acceptable":[211],"detection":[217],"while":[221],"having":[222],"significantly":[224],"smaller":[225],"number":[226],"parameters":[228],"floating-point":[230],"operations":[231],"comparing":[232],"many":[234],"CNN-based":[235],"models.":[237]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2021-03-15T00:00:00"}