{"id":"https://openalex.org/W2970101707","doi":"https://doi.org/10.1109/icip.2019.8803443","title":"A Cascaded Noise-Robust Deep CNN for Face Recognition","display_name":"A Cascaded Noise-Robust Deep CNN for Face Recognition","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970101707","doi":"https://doi.org/10.1109/icip.2019.8803443","mag":"2970101707"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5061802840","display_name":"Xiangbang Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangbang Meng","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073464445","display_name":"Si Chen","orcid":"https://orcid.org/0000-0002-5631-7942"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Chen","raw_affiliation_strings":["Xiamen University of Technology, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University of Technology, Xiamen, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3487","last_page":"3491"},"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":0.9983999729156494,"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/T10860","display_name":"Speech and Audio Processing","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7947835922241211},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7699388265609741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7372808456420898},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.7172886729240417},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6849815845489502},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6708033084869385},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6619540452957153},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6174353361129761},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5827173590660095},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4718335270881653},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46586182713508606},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4356890916824341},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2321741282939911}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7947835922241211},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7699388265609741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7372808456420898},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.7172886729240417},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6849815845489502},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6708033084869385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6619540452957153},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6174353361129761},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5827173590660095},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4718335270881653},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46586182713508606},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4356890916824341},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2321741282939911},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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/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/icip.2019.8803443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1509966554","https://openalex.org/W1584711673","https://openalex.org/W1782590233","https://openalex.org/W1836465849","https://openalex.org/W2033419168","https://openalex.org/W2048695508","https://openalex.org/W2056370875","https://openalex.org/W2069165391","https://openalex.org/W2096733369","https://openalex.org/W2097117768","https://openalex.org/W2145287260","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2341528187","https://openalex.org/W2508457857","https://openalex.org/W2609575245","https://openalex.org/W2613155248","https://openalex.org/W2724525935","https://openalex.org/W2755396225","https://openalex.org/W2784163702","https://openalex.org/W2963446712","https://openalex.org/W2963466847","https://openalex.org/W3099206234","https://openalex.org/W3101227480","https://openalex.org/W3101998545","https://openalex.org/W3103152812","https://openalex.org/W6630649318","https://openalex.org/W6638667902","https://openalex.org/W6662532501","https://openalex.org/W6681342084","https://openalex.org/W6684191040","https://openalex.org/W6736780073"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W2985118265"],"abstract_inverted_index":{"State-of-the-art":[0],"face":[1,18,32,59,63,99,133],"recognition":[2,33,60,64,100],"methods":[3],"have":[4],"achieved":[5],"excellent":[6],"performance":[7,29,138],"on":[8,131],"the":[9,16,28,71,85,89,95,98,103,110,115,122,136,140],"clean":[10],"datasets.":[11],"However,":[12],"in":[13,78,114],"real-world":[14],"applications,":[15],"captured":[17],"images":[19,86],"are":[20,92],"usually":[21],"contaminated":[22],"with":[23],"noise,":[24],"which":[25,118],"significantly":[26],"decreases":[27],"of":[30,50,68,97,127,139],"these":[31],"methods.":[34,146],"In":[35],"this":[36],"paper,":[37],"we":[38,74],"propose":[39],"a":[40,54,58,79,83],"cascaded":[41,80],"noise-robust":[42],"deep":[43,125],"convolutional":[44],"neural":[45],"network":[46],"(CNR-CNN)":[47],"method,":[48],"consisting":[49],"two":[51,72],"sub-networks,":[52,73],"i.e.,":[53],"denoising":[55,90,116],"sub-network":[56,91],"and":[57,124],"sub-network,":[61,117],"for":[62],"under":[65],"noise.":[66],"Instead":[67],"separately":[69],"training":[70,96],"jointly":[75],"train":[76],"them":[77],"manner.":[81],"As":[82],"result,":[84],"generated":[87],"from":[88],"beneficial":[93],"to":[94,108],"sub-network.":[101],"Furthermore,":[102],"dense":[104],"connectivity":[105],"is":[106],"used":[107],"concatenate":[109],"feature":[111],"maps":[112],"layer-by-layer":[113],"can":[119],"effectively":[120],"exploit":[121],"shallow":[123],"features":[126],"CNN.":[128],"Experimental":[129],"results":[130],"public":[132],"datasets":[134],"demonstrate":[135],"superior":[137],"proposed":[141],"method":[142],"over":[143],"several":[144],"state-of-the-art":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}