{"id":"https://openalex.org/W2579617619","doi":"https://doi.org/10.1109/apsipa.2016.7820742","title":"Head pose estimation using random forest and texture analysis","display_name":"Head pose estimation using random forest and texture analysis","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2579617619","doi":"https://doi.org/10.1109/apsipa.2016.7820742","mag":"2579617619"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2016.7820742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2016.7820742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088157446","display_name":"Kang Min-Joo","orcid":null},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min-Joo Kang","raw_affiliation_strings":["Ewha Womans University, Seoul, KR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ewha Womans University, Seoul, KR","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088571985","display_name":"Ha-Yeon Lee","orcid":"https://orcid.org/0000-0003-4648-586X"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ha-Yeon Lee","raw_affiliation_strings":["Ewha Womans University, Seoul, KR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ewha Womans University, Seoul, KR","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039192390","display_name":"Je\u2010Won Kang","orcid":"https://orcid.org/0000-0002-1637-9479"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Je-Won Kang","raw_affiliation_strings":["Ewha Womans University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ewha Womans University, Seoul, Korea","institution_ids":["https://openalex.org/I138925566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I138925566"],"apc_list":null,"apc_paid":null,"fwci":0.1006,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45471546,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9980999827384949,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9775999784469604,"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/random-forest","display_name":"Random forest","score":0.794735848903656},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.7446238994598389},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7010261416435242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6589671969413757},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6292834877967834},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5698865652084351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5382087230682373},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5145708918571472},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5052579045295715},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.49996018409729004},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4690696895122528},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4611537456512451},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4474659562110901},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.44302451610565186},{"id":"https://openalex.org/keywords/head","display_name":"Head (geology)","score":0.41285377740859985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25058186054229736},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07793635129928589},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07193136215209961}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.794735848903656},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.7446238994598389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010261416435242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6589671969413757},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6292834877967834},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5698865652084351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5382087230682373},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5145708918571472},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5052579045295715},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.49996018409729004},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4690696895122528},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4611537456512451},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4474659562110901},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.44302451610565186},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.41285377740859985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25058186054229736},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07793635129928589},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07193136215209961},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2016.7820742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2016.7820742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1974744233","https://openalex.org/W1986803802","https://openalex.org/W1996478295","https://openalex.org/W2059916030","https://openalex.org/W2085390190","https://openalex.org/W2085730440","https://openalex.org/W2086618980","https://openalex.org/W2096171208","https://openalex.org/W2125337786","https://openalex.org/W2131081720","https://openalex.org/W2145287260","https://openalex.org/W2155775419","https://openalex.org/W2163352848","https://openalex.org/W2911964244","https://openalex.org/W2912990735"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W3135126032","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487"],"abstract_inverted_index":{"In":[0,24],"this":[1],"paper,":[2],"we":[3,28,88],"propose":[4],"a":[5,32,50,59,90,130],"new":[6],"head":[7,145],"pose":[8,146],"estimation":[9,41,147],"technique":[10,27,139],"based":[11],"on":[12],"Random":[13],"Forest":[14],"(RF)":[15],"and":[16,43,47,62,65,74,155],"Multi-scale":[17],"Block":[18,20],"Local":[19],"Pattern":[21],"(MB-LBP)":[22],"features.":[23],"the":[25,40,70,75,79,85,94,97,106,110,126,137,144],"proposed":[26,138],"aim":[29],"to":[30,38,101,108],"learn":[31],"randomized":[33,117],"tree":[34,107],"with":[35],"useful":[36],"attributes":[37],"improve":[39],"accuracy":[42],"tolerance":[44],"of":[45,52,96,105,151],"occlusions":[46],"illumination.":[48],"Precisely,":[49],"number":[51],"MB-LBP":[53,71],"feature":[54],"spaces":[55],"are":[56,81,123],"generated":[57],"from":[58],"face":[60],"image,":[61],"random":[63,66],"inputs":[64],"features":[67],"such":[68],"as":[69],"scale":[72],"parameter":[73],"block":[76],"coordinate":[77],"in":[78,121,129,143,148],"pool":[80],"used":[82,124],"for":[83,125],"building":[84],"tree.":[86],"Furthermore":[87],"develop":[89],"split":[91],"function":[92],"considering":[93],"properties":[95],"uniform":[98],"LBP,":[99],"applied":[100],"each":[102],"internal":[103],"node":[104],"maximize":[109],"information":[111],"gain":[112],"at":[113],"that":[114,136],"node.":[115],"The":[116],"trees":[118],"put":[119],"together":[120],"RF":[122],"final":[127],"decision":[128],"Maximum-A-Posteriori":[131],"criterion.":[132],"Experimental":[133],"results":[134],"demonstrate":[135],"provides":[140],"impressive":[141],"performance":[142],"various":[149],"conditions":[150],"illumination,":[152],"poses,":[153],"expressions,":[154],"facial":[156],"occlusions.":[157]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}