{"id":"https://openalex.org/W2890304188","doi":"https://doi.org/10.1109/icip.2018.8451026","title":"A Two-Step Learning Method for Detecting Landmarks on Faces from Different Domains","display_name":"A Two-Step Learning Method for Detecting Landmarks on Faces from Different Domains","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2890304188","doi":"https://doi.org/10.1109/icip.2018.8451026","mag":"2890304188"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1809.04621","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075186953","display_name":"Bruna Vieira Frade","orcid":"https://orcid.org/0000-0002-4951-7882"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruna Vieira Frade","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002613601","display_name":"Erickson R. Nascimento","orcid":"https://orcid.org/0000-0003-2973-2232"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Erickson R. Nascimento","raw_affiliation_strings":["Universidade Federal de Minas Gerais (UFMG), Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais (UFMG), Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I110200422"],"apc_list":null,"apc_paid":null,"fwci":0.212,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56014119,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"2655","last_page":"2659"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","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/T11448","display_name":"Face recognition and analysis","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/T10828","display_name":"Biometric Identification and Security","score":0.9923999905586243,"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/T10057","display_name":"Face and Expression Recognition","score":0.9843000173568726,"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/leverage","display_name":"Leverage (statistics)","score":0.8036502003669739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7894666194915771},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7538002729415894},{"id":"https://openalex.org/keywords/fiducial-marker","display_name":"Fiducial marker","score":0.7533137202262878},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7459890246391296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5195896625518799},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.46852487325668335},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4423586428165436},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4311198592185974},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4296773076057434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3643893897533417},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.13921180367469788},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10674279928207397},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09898990392684937}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8036502003669739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7894666194915771},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7538002729415894},{"id":"https://openalex.org/C173974348","wikidata":"https://www.wikidata.org/wiki/Q1469893","display_name":"Fiducial marker","level":2,"score":0.7533137202262878},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7459890246391296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5195896625518799},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.46852487325668335},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4423586428165436},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4311198592185974},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4296773076057434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3643893897533417},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.13921180367469788},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10674279928207397},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09898990392684937},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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":2,"locations":[{"id":"doi:10.1109/icip.2018.8451026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1809.04621","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.04621","pdf_url":"https://arxiv.org/pdf/1809.04621","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1809.04621","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.04621","pdf_url":"https://arxiv.org/pdf/1809.04621","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1855204404","https://openalex.org/W1896424170","https://openalex.org/W1963599662","https://openalex.org/W1976948919","https://openalex.org/W2032558548","https://openalex.org/W2047508432","https://openalex.org/W2122007052","https://openalex.org/W2147334734","https://openalex.org/W2166694921","https://openalex.org/W2345945060","https://openalex.org/W2478454054","https://openalex.org/W2607421098","https://openalex.org/W2952074561","https://openalex.org/W2952729838","https://openalex.org/W2964021040","https://openalex.org/W4252995899","https://openalex.org/W4297915247","https://openalex.org/W6638958570","https://openalex.org/W6639495226","https://openalex.org/W6662335928","https://openalex.org/W6684333976","https://openalex.org/W6694125527","https://openalex.org/W6721237847","https://openalex.org/W6764072534"],"related_works":["https://openalex.org/W4283211831","https://openalex.org/W4389474468","https://openalex.org/W2798287483","https://openalex.org/W2963261224","https://openalex.org/W4300172004","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867"],"abstract_inverted_index":{"The":[0,84],"detection":[1,106],"of":[2,18,30,32,41,76,94,107,115],"fiducial":[3,60],"points":[4,61],"on":[5,37,54,62,71],"faces":[6,79],"has":[7],"significantly":[8],"been":[9],"favored":[10],"by":[11],"the":[12,16,24,28,33,95,105,110],"rapid":[13],"progress":[14],"in":[15,21,23],"field":[17],"machine":[19],"learning,":[20],"particular":[22],"convolution":[25],"networks.":[26],"However,":[27],"accuracy":[29],"most":[31],"detectors":[34],"strongly":[35],"depends":[36],"an":[38],"enormous":[39],"amount":[40],"annotated":[42,101,116],"data.":[43,117],"In":[44],"this":[45],"work,":[46],"we":[47],"present":[48],"a":[49,55],"domain":[50],"adaptation":[51],"approach":[52],"based":[53],"two-step":[56],"learning":[57],"to":[58,103],"detect":[59],"human":[63],"and":[64,82,97],"animal":[65,78],"faces.":[66],"We":[67],"evaluate":[68],"our":[69,88],"method":[70,89],"three":[72],"different":[73,77],"datasets":[74],"composed":[75],"(cats,":[80],"dogs,":[81],"horses).":[83],"experiments":[85],"show":[86],"that":[87],"performs":[90],"better":[91],"than":[92],"state":[93],"art":[96],"can":[98],"use":[99],"few":[100],"data":[102],"leverage":[104],"landmarks":[108],"reducing":[109],"demand":[111],"for":[112],"large":[113],"volume":[114]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
