{"id":"https://openalex.org/W2911219917","doi":"https://doi.org/10.1109/dicta.2018.8615831","title":"Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network","display_name":"Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2911219917","doi":"https://doi.org/10.1109/dicta.2018.8615831","mag":"2911219917"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2018.8615831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2018.8615831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},"type":"article","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/A5083460237","display_name":"Muhammad Asif","orcid":"https://orcid.org/0000-0003-1839-2527"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"M. Daud Abdullah Asif","raw_affiliation_strings":["School of Engineering, Griffith Univeristy, Queensland, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Griffith Univeristy, Queensland, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071549690","display_name":"Yongsheng Gao","orcid":"https://orcid.org/0000-0002-5382-5351"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yongsheng Gao","raw_affiliation_strings":["School of Engineering, Griffith Univeristy, Queensland, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Griffith Univeristy, Queensland, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100781212","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-5822-8233"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["School of Information Communication Technology, Griffith University, Queensland, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Communication Technology, Griffith University, Queensland, Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083460237"],"corresponding_institution_ids":["https://openalex.org/I11701301"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48632063,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9991999864578247,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9930999875068665,"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/computer-science","display_name":"Computer science","score":0.8061828017234802},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8029442429542542},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7597059011459351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7156656384468079},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6604664921760559},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6478906869888306},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5869545936584473},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5252739191055298},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5180875658988953},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5034403204917908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061828017234802},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8029442429542542},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7597059011459351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7156656384468079},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6604664921760559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6478906869888306},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5869545936584473},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5252739191055298},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5180875658988953},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5034403204917908},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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":"doi:10.1109/dicta.2018.8615831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2018.8615831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/384217","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/384217","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1964762821","https://openalex.org/W1977247653","https://openalex.org/W1991385629","https://openalex.org/W1993655741","https://openalex.org/W2006793117","https://openalex.org/W2037064747","https://openalex.org/W2057747179","https://openalex.org/W2059652177","https://openalex.org/W2073609604","https://openalex.org/W2097437748","https://openalex.org/W2097777575","https://openalex.org/W2104294146","https://openalex.org/W2120372495","https://openalex.org/W2121647436","https://openalex.org/W2123921160","https://openalex.org/W2125874614","https://openalex.org/W2138451337","https://openalex.org/W2145947562","https://openalex.org/W2160254522","https://openalex.org/W2163352848","https://openalex.org/W2163605009","https://openalex.org/W2163808566","https://openalex.org/W2166916857","https://openalex.org/W2325939864","https://openalex.org/W2513491952","https://openalex.org/W2585123518","https://openalex.org/W2798653614","https://openalex.org/W2798807671","https://openalex.org/W2963551763","https://openalex.org/W2963861381","https://openalex.org/W2963958000","https://openalex.org/W2964121744","https://openalex.org/W6639204139","https://openalex.org/W6684191040","https://openalex.org/W6684554773","https://openalex.org/W6700903540","https://openalex.org/W6749445441","https://openalex.org/W6750685679","https://openalex.org/W6750716683"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2384651879"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,128],"propose":[4,129],"a":[5,21,29,41,53,92,104,130],"novel":[6],"Local":[7],"Mesh":[8],"High-order":[9],"Pattern":[10],"Descriptor":[11],"(LMHPD)":[12],"for":[13,165],"face":[14,145,166],"recognition.":[15],"This":[16],"description":[17],"is":[18,26,61,86,100],"constructed":[19],"in":[20,74,82,102],"high-order":[22,134],"derivative":[23],"space":[24],"and":[25,50,88,122,153],"integrated":[27],"with":[28,47,76,91,110,137],"Convolutional":[30],"Neural":[31],"Network":[32],"(CNN)":[33],"architecture.":[34],"Based":[35],"on":[36,149],"the":[37,58,77,83,113,119,126],"information":[38],"collected":[39],"at":[40,125],"local":[42],"neighborhood":[43],"of":[44,57,95,112,133],"reference":[45,59],"pixel":[46,60],"diverse":[48],"radiuses":[49],"mesh":[51],"angles,":[52],"vectorized":[54],"feature":[55],"representation":[56],"generated":[62],"to":[63,70,72],"provide":[64],"micro-patterns.":[65],"They":[66],"are":[67],"then":[68],"converted":[69],"multi-channels":[71],"use":[73],"conjunction":[75],"CNN.":[78],"The":[79],"CNN":[80,115,138],"adopted":[81],"proposed":[84],"architecture":[85,139],"generic":[87],"very":[89],"compact":[90],"small":[93],"number":[94],"convolutional":[96],"layers.":[97],"However,":[98],"LMHPD":[99],"derived":[101],"such":[103],"way":[105],"that":[106,140,157],"it":[107],"can":[108,141],"work":[109],"most":[111],"available":[114],"architectures.":[116],"For":[117],"keeping":[118],"computational":[120],"cost":[121],"time":[123],"complexity":[124],"minimum,":[127],"lighter":[131],"approach":[132],"texture":[135],"descriptor":[136],"effectively":[142],"extract":[143],"discriminative":[144],"features.":[146],"Extensive":[147],"experiments":[148],"Extended":[150],"Yale":[151],"B":[152],"CMU-PIE":[154],"datasets":[155],"show":[156],"our":[158],"method":[159],"consistently":[160],"outperforms":[161],"several":[162],"alternative":[163],"descriptors":[164],"recognition":[167],"under":[168],"various":[169],"circumstances.":[170]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}