{"id":"https://openalex.org/W2947699212","doi":"https://doi.org/10.2312/3dor.20191062","title":"Depth-Based Face Recognition by Learning from 3D-LBP Images","display_name":"Depth-Based Face Recognition by Learning from 3D-LBP Images","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2947699212","doi":"https://doi.org/10.2312/3dor.20191062","mag":"2947699212"},"language":"en","primary_location":{"id":"pmh:oai:flore.unifi.it:2158/1167286","is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/1167286","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"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":"info:eu-repo/semantics/conferenceObject"},"type":"conference-paper","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/3dor.20191062","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020839282","display_name":"Jo\u00e3o Baptista Cardia Neto","orcid":"https://orcid.org/0000-0002-2727-1383"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Neto, Joao Baptista Cardia","raw_affiliation_strings":["Universidade Federal de S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021585095","display_name":"Aparecido Nilceu Marana","orcid":"https://orcid.org/0000-0003-4861-7061"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marana, Aparecido Nilceu","raw_affiliation_strings":["Universidade Estadual Paulista (Unesp), S\u00e3o Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Estadual Paulista (Unesp), S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028907737","display_name":"Claudio Ferrari","orcid":"https://orcid.org/0000-0001-9465-6753"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ferrari, Claudio","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013110565","display_name":"Stefano Berretti","orcid":"https://orcid.org/0000-0003-1219-4386"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Berretti, Stefano","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081506611","display_name":"Alberto Del Bimbo","orcid":"https://orcid.org/0000-0002-1052-8322"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Bimbo, Alberto Del","raw_affiliation_strings":["University of Florence, Florence, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florence, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"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":0.7329999804496765,"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":0.7329999804496765,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.6783000230789185,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6763216257095337},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6622697710990906},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6564199924468994},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.631824791431427},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5312768220901489},{"id":"https://openalex.org/keywords/three-dimensional-face-recognition","display_name":"Three-dimensional face recognition","score":0.456174373626709},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4160040616989136},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.375213623046875}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6763216257095337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6622697710990906},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6564199924468994},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.631824791431427},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5312768220901489},{"id":"https://openalex.org/C88799230","wikidata":"https://www.wikidata.org/wiki/Q3398329","display_name":"Three-dimensional face recognition","level":5,"score":0.456174373626709},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4160040616989136},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.375213623046875},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:flore.unifi.it:2158/1167286","is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/1167286","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"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":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.2312/3dor.20191062","is_oa":true,"landing_page_url":"https://doi.org/10.2312/3dor.20191062","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.2312/3dor.20191062","is_oa":true,"landing_page_url":"https://doi.org/10.2312/3dor.20191062","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1982042492","https://openalex.org/W2733060750","https://openalex.org/W3165495218","https://openalex.org/W2135557971","https://openalex.org/W2057517930","https://openalex.org/W2964589334","https://openalex.org/W3006447075","https://openalex.org/W3045933645","https://openalex.org/W2963094609","https://openalex.org/W2745309258","https://openalex.org/W1968071942","https://openalex.org/W2117879155","https://openalex.org/W1573760330","https://openalex.org/W2292645027","https://openalex.org/W2819236253","https://openalex.org/W2888580418","https://openalex.org/W1951319388","https://openalex.org/W2059269112","https://openalex.org/W2560211560","https://openalex.org/W2936721683"],"abstract_inverted_index":{"In":[0,190],"this":[1,73,191],"paper,":[2],"we":[3,75,195],"propose":[4,77],"a":[5,66,78,89,93,187],"hybrid":[6],"framework":[7],"for":[8,72,178],"face":[9,217],"recognition":[10],"from":[11,105],"depth":[12,35,54,86,122,139,216],"images,":[13],"which":[14],"is":[15,30],"both":[16],"effective":[17],"and":[18,40,202,208,214,241],"efficient.":[19],"It":[20],"consists":[21],"of":[22,37,80,118,124,135,144,160,171,244],"two":[23,111],"main":[24,112],"stages:":[25],"First,":[26],"the":[27,33,38,44,59,81,106,115,125,128,131,137,145,157,161,164,166,172,176,179,182,193,221,232,245],"3DLBP":[28],"operator":[29,51,83],"applied":[31],"to":[32,42,58,64,121,231],"raw":[34,138],"data":[36],"face,":[39,146,173],"used":[41],"build":[43],"corresponding":[45],"descriptor":[46],"images":[47,123],"(DIs).":[48],"However,":[49],"such":[50],"quantizes":[52],"relative":[53],"differences":[55,87],"over/under":[56],"+-7":[57],"same":[60],"bin,":[61],"so":[62],"as":[63,181],"generate":[65],"fixed":[67],"dimensional":[68],"descriptor.":[69],"To":[70],"account":[71],"behavior,":[74],"also":[76],"modification":[79],"traditional":[82],"that":[84,103,226],"encodes":[85],"using":[88,234],"sigmoid":[90],"function.":[91],"Then,":[92],"not-so-deep":[94],"(shallow)":[95],"convolutional":[96],"neural":[97],"network":[98],"(SCNN)":[99],"has":[100,198],"been":[101],"designed":[102,197],"learns":[104],"DIs.":[107],"This":[108,152],"architecture":[109],"showed":[110],"advantages":[113],"over":[114],"direct":[116],"application":[117],"deep-CNN":[119],"(DCNN)":[120],"face:":[126],"On":[127,163],"one":[129],"hand,":[130],"DIs":[132,167],"are":[133,227],"capable":[134],"enriching":[136],"data,":[140,239],"emphasizing":[141],"relevant":[142],"traits":[143],"while":[147],"reducing":[148],"their":[149],"acquisition":[150],"noise.":[151],"resulted":[153],"decisive":[154],"in":[155,186],"improving":[156],"learning":[158],"capability":[159],"network;":[162],"other,":[165],"capture":[168],"low-level":[169],"features":[170],"thus":[174],"playing":[175],"role":[177],"SCNN":[180,194],"first":[183],"layers":[184,201],"do":[185],"DCNN":[188],"architecture.":[189],"way,":[192],"have":[196],"much":[199],"less":[200,237],"can":[203],"be":[204],"trained":[205],"more":[206],"easily":[207],"faster.":[209],"Extensive":[210],"experiments":[211],"on":[212],"low-":[213],"high-resolution":[215],"datasets":[218],"confirmed":[219],"us":[220],"above":[222],"advantages,":[223],"showing":[224],"results":[225],"comparable":[228],"or":[229],"superior":[230],"state-of-the-art,":[233],"by":[235],"far":[236],"training":[238],"time,":[240],"memory":[242],"occupancy":[243],"network.":[246]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
