{"id":"https://openalex.org/W3000589903","doi":"https://doi.org/10.1145/3373477.3373697","title":"Block-regressed face alignment algorithm based on supervised descent method","display_name":"Block-regressed face alignment algorithm based on supervised descent method","publication_year":2019,"publication_date":"2019-11-15","ids":{"openalex":"https://openalex.org/W3000589903","doi":"https://doi.org/10.1145/3373477.3373697","mag":"3000589903"},"language":"en","primary_location":{"id":"doi:10.1145/3373477.3373697","doi":"https://doi.org/10.1145/3373477.3373697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373477.3373697","pdf_url":null,"source":{"id":"https://openalex.org/S7407089110","display_name":"International Conference on Advanced Information Science and System (AISS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Advanced Information Science and System","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5028998390","display_name":"Yuqi Shi","orcid":"https://orcid.org/0009-0007-1247-4165","observed_orcids":["https://orcid.org/0009-0007-1247-4165"]},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuqi Shi","raw_affiliation_strings":["Wuhan University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I43922553"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5028998390"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"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":"5"},"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/T10057","display_name":"Face and Expression Recognition","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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/facial-landmark-detection","display_name":"facial landmark detection","score":0.9660000205039978},{"id":"https://openalex.org/keywords/supervised-descent-method","display_name":"supervised descent method","score":0.6800000071525574},{"id":"https://openalex.org/keywords/regression-loss-function","display_name":"regression loss function","score":0.5130000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7391509413719177},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.7119821310043335},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6858294606208801},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.6704173684120178},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.593210756778717},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5929513573646545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5535316467285156},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5411902666091919},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.46882033348083496},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46213167905807495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4541274607181549},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.45232850313186646},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4326028823852539},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3451805114746094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18702492117881775},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1297016739845276},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3373477.3373697","doi":"https://doi.org/10.1145/3373477.3373697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373477.3373697","pdf_url":null,"source":{"id":"https://openalex.org/S7407089110","display_name":"International Conference on Advanced Information Science and System (AISS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Advanced Information Science and System","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1896424170","https://openalex.org/W1976948919","https://openalex.org/W2002406878","https://openalex.org/W2016043834","https://openalex.org/W2151103935","https://openalex.org/W2157285372","https://openalex.org/W2889234131","https://openalex.org/W2963466847","https://openalex.org/W3106743041"],"related_works":["https://openalex.org/W1503624268","https://openalex.org/W2547715144","https://openalex.org/W2412369746","https://openalex.org/W2510750874","https://openalex.org/W3001602698","https://openalex.org/W2906062526","https://openalex.org/W2283863529","https://openalex.org/W3154502638","https://openalex.org/W2964571041","https://openalex.org/W2233669653"],"abstract_inverted_index":{"In":[0,38,115],"recent":[1],"years,":[2],"the":[3,14,34,48,63,75,86,90,103,106,111,118,134,149],"accuracy":[4,36,156],"of":[5,16,51,62,105,120,140],"face":[6,20,108],"alignment":[7,135],"has":[8],"been":[9],"improved":[10],"a":[11,43,98],"lot.":[12],"However,":[13],"landmarks":[15,53],"each":[17,56,72],"component":[18,57,73],"in":[19,24,153],"are":[21,69,125],"trained":[22],"together":[23],"most":[25],"algorithms,":[26],"which":[27,130],"ignore":[28],"their":[29],"dedicated":[30],"characteristic":[31],"and":[32,110,122,157],"limit":[33],"further":[35,132],"improvement.":[37],"this":[39,116],"paper,":[40],"we":[41],"propose":[42],"new":[44],"approach":[45],"to":[46,100],"improve":[47],"localization":[49],"performance":[50],"facial":[52],"by":[54],"taking":[55],"as":[58],"independent":[59,67,82,112,123],"task":[60],"instead":[61],"whole":[64,107],"face.":[65],"Namely,":[66],"regressors":[68,83],"learned":[70],"for":[71,84],"using":[74],"gradient":[76],"descent":[77],"method.":[78],"If":[79],"only":[80],"considering":[81],"component,":[85],"inherent":[87],"correlation":[88],"between":[89],"components":[91,113],"may":[92],"be":[93],"neglected.":[94],"This":[95],"paper":[96],"proposed":[97],"strategy":[99],"effectively":[101],"combine":[102],"results":[104,124],"regression":[109],"regressions.":[114],"way,":[117],"effect":[119],"holistic":[121],"all":[126],"taken":[127],"into":[128],"consideration,":[129],"can":[131],"enhance":[133],"accuracy.":[136],"A":[137],"large":[138],"number":[139],"experiments":[141],"show":[142],"that":[143],"our":[144],"method":[145],"is":[146],"better":[147],"than":[148],"single":[150],"loss":[151],"function":[152],"both":[154],"detection":[155],"reliability.":[158]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2020-01-23T00:00:00"}
