Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition
•Face recognition success rate is limited by many environmental factors.•Low generalization ability of a single convolutional neural network is disadvantageous.•A new parallel ensemble learning model combines local binary patterns into CNN.•The proposed method is fused with a pedestrian detection mo...
Saved in:
| Published in: | Computer methods and programs in biomedicine Vol. 197; p. 105622 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Ireland
Elsevier B.V
01.12.2020
|
| Subjects: | |
| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Face recognition success rate is limited by many environmental factors.•Low generalization ability of a single convolutional neural network is disadvantageous.•A new parallel ensemble learning model combines local binary patterns into CNN.•The proposed method is fused with a pedestrian detection model as a hybrid model.•Popular ORL and Yale-B face databases are tested, with accuracies 100% and 97.51%.
Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.
First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting.
By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%.
In summary, the proposed approach greatly outperforms other competitive methods. |
|---|---|
| AbstractList | •Face recognition success rate is limited by many environmental factors.•Low generalization ability of a single convolutional neural network is disadvantageous.•A new parallel ensemble learning model combines local binary patterns into CNN.•The proposed method is fused with a pedestrian detection model as a hybrid model.•Popular ORL and Yale-B face databases are tested, with accuracies 100% and 97.51%.
Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.
First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting.
By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%.
In summary, the proposed approach greatly outperforms other competitive methods. Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.BACKGROUND AND OBJECTIVEFace recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting.METHODSFirst, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting.By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%.RESULTSBy this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%.In summary, the proposed approach greatly outperforms other competitive methods.CONCLUSIONIn summary, the proposed approach greatly outperforms other competitive methods. Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. In summary, the proposed approach greatly outperforms other competitive methods. |
| ArticleNumber | 105622 |
| Author | Fong, Simon Tang, Jialin Su, Qinglang Su, Binghua Gong, Xueyuan Cao, Wei |
| Author_xml | – sequence: 1 givenname: Jialin surname: Tang fullname: Tang, Jialin email: thong03@qq.com organization: Beijing Institute of Technology, Zhuhai 519088, China – sequence: 2 givenname: Qinglang surname: Su fullname: Su, Qinglang email: sonnysu@cityu.mo organization: City University of Macau, Macau, China – sequence: 3 givenname: Binghua surname: Su fullname: Su, Binghua email: 01004@bitzh.edu.cn organization: Beijing Institute of Technology, Zhuhai 519088, China – sequence: 4 givenname: Simon surname: Fong fullname: Fong, Simon email: ccfong@um.edu.mo organization: Department of Computer and Information Science, University of Macau, Macau, China – sequence: 5 givenname: Wei surname: Cao fullname: Cao, Wei email: 17303@bitzh.edu.cn organization: Beijing Institute of Technology, Zhuhai 519088, China – sequence: 6 givenname: Xueyuan surname: Gong fullname: Gong, Xueyuan email: 18202@bitzh.edu.cn organization: Beijing Institute of Technology, Zhuhai 519088, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32629293$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkUFv1DAUhC1URLeFP8AB-cgli_0cJ1nEBVVQkCrBAc6WY79U3jr2YjtF_fc43fbSQzk96Wm-kWbmjJyEGJCQt5xtOePdh_3WzIdxCwzWh-wAXpANH3poetnJE7Kpol0DHetPyVnOe8YYSNm9IqcCOtjBTmzI_qdO2nv0FEPGefRIPeoUXLimcaImhtvol-Ji0J4GXNL9KX9juslUB0t9NPU1uqDTHT3oUjCFTKeY6KQN0oQmXge3GrwmLyftM755uOfk99cvvy6-NVc_Lr9ffL5qTNuL0thBTNDhBFJI2_Vj2wKygaORPRfYjZpb3lth9Tj1vOV8lBpwgKGG4ygGK87J-6PvIcU_C-aiZpcNeq8DxiUraIFzzlo-VOm7B-kyzmjVIbm55lCP_VTBcBSYFHNOOCnjil7TlKSdV5ypdQq1V-sUap1CHaeoKDxBH92fhT4dIawF3TpMKhuHwaB1tcmibHTP4x-f4Ma74OpCN3j3P_gf17i2SA |
| CitedBy_id | crossref_primary_10_32604_cmc_2021_018709 crossref_primary_10_1007_s10479_024_06444_0 crossref_primary_10_1016_j_media_2022_102438 crossref_primary_10_1051_e3sconf_202344802042 crossref_primary_10_1002_cav_2094 crossref_primary_10_1016_j_cmpbup_2025_100195 crossref_primary_10_1177_14680874241288626 crossref_primary_10_1109_ACCESS_2024_3368031 crossref_primary_10_1016_j_cmpb_2021_106112 crossref_primary_10_1186_s12880_025_01667_4 crossref_primary_10_1109_ACCESS_2022_3184616 crossref_primary_10_1007_s13042_022_01751_z crossref_primary_10_3390_s25175512 crossref_primary_10_3390_math9222889 crossref_primary_10_3233_JIFS_210130 crossref_primary_10_46604_ijeti_2021_6174 crossref_primary_10_1007_s42044_020_00076_w crossref_primary_10_1109_JBHI_2023_3266587 crossref_primary_10_3390_e23111390 crossref_primary_10_1016_j_patcog_2023_109959 crossref_primary_10_1002_cpe_7916 crossref_primary_10_1016_j_ipm_2023_103545 crossref_primary_10_1016_j_jksuci_2023_01_014 |
| Cites_doi | 10.1109/TPAMI.2006.244 10.1109/TPAMI.2002.1017623 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier B.V. Copyright © 2020 Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2020 Elsevier B.V. – notice: Copyright © 2020 Elsevier B.V. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.cmpb.2020.105622 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1872-7565 |
| ExternalDocumentID | 32629293 10_1016_j_cmpb_2020_105622 S0169260720314553 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M -~X .1- .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HMK HMO HVGLF HZ~ IHE J1W KOM LG9 M29 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SBC SDF SDG SEL SES SEW SPC SPCBC SSH SSV SSZ T5K UHS WUQ XPP Z5R ZGI ZY4 ~G- ~HD AACTN AAIAV ABLVK ABTAH ABYKQ AFKWA AJBFU AJOXV AMFUW LCYCR RIG 9DU AAYXX CITATION AFCTW CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c473t-d83f26ef2535d67b442e081ec5713e6ba1d17d3dabf71411b5a2e8280021e38d3 |
| ISICitedReferencesCount | 32 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000594824200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0169-2607 1872-7565 |
| IngestDate | Sun Sep 28 04:26:26 EDT 2025 Thu Apr 03 07:02:15 EDT 2025 Sat Nov 29 07:25:41 EST 2025 Tue Nov 18 22:36:30 EST 2025 Fri Feb 23 02:45:35 EST 2024 Tue Oct 14 19:31:46 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Local Binary Patterns (LBP) Convolutional Neural Networks (CNN) Face recognition Ensemble learning |
| Language | English |
| License | Copyright © 2020 Elsevier B.V. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c473t-d83f26ef2535d67b442e081ec5713e6ba1d17d3dabf71411b5a2e8280021e38d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 32629293 |
| PQID | 2421110418 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2421110418 pubmed_primary_32629293 crossref_citationtrail_10_1016_j_cmpb_2020_105622 crossref_primary_10_1016_j_cmpb_2020_105622 elsevier_sciencedirect_doi_10_1016_j_cmpb_2020_105622 elsevier_clinicalkey_doi_10_1016_j_cmpb_2020_105622 |
| PublicationCentury | 2000 |
| PublicationDate | December 2020 2020-12-00 2020-Dec 20201201 |
| PublicationDateYYYYMMDD | 2020-12-01 |
| PublicationDate_xml | – month: 12 year: 2020 text: December 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Ireland |
| PublicationPlace_xml | – name: Ireland |
| PublicationTitle | Computer methods and programs in biomedicine |
| PublicationTitleAlternate | Comput Methods Programs Biomed |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ioffe, Szegedy (bib0010) 2015 Liu, Chen, Sun (bib0004) 2017 He, Zhang, Ren, Sun (bib0007) 2016 Last accessed: 10/27/2019. Dalal, Triggs (bib0013) 2005 Ojala, Pietikainen, Maenpaa (bib0008) 2002; 24 CBCL pedestrian database, website Wang, Wang, Li (bib0005) 2017 Ahonen, Hadid, Pietikainen (bib0003) 2006; 28 Szegedy, Liu, Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke, Rabinovich. (bib0009) 2015 Parmar, Mehta (bib0002) 2014 Ahamed, Alam, Islam (bib0006) 2018 Szegedy, Vanhoucke, Ioffe, Shlens, Wojna (bib0011) 2016 Tahira, Asif (bib0001) 2018 Liu (10.1016/j.cmpb.2020.105622_bib0004) 2017 10.1016/j.cmpb.2020.105622_bib0012 Parmar (10.1016/j.cmpb.2020.105622_bib0002) 2014 Ojala (10.1016/j.cmpb.2020.105622_bib0008) 2002; 24 He (10.1016/j.cmpb.2020.105622_bib0007) 2016 Dalal (10.1016/j.cmpb.2020.105622_bib0013) 2005 Ahamed (10.1016/j.cmpb.2020.105622_bib0006) 2018 Szegedy (10.1016/j.cmpb.2020.105622_bib0011) 2016 Ahonen (10.1016/j.cmpb.2020.105622_bib0003) 2006; 28 Tahira (10.1016/j.cmpb.2020.105622_bib0001) 2018 Szegedy (10.1016/j.cmpb.2020.105622_bib0009) 2015 Wang (10.1016/j.cmpb.2020.105622_bib0005) 2017 Ioffe (10.1016/j.cmpb.2020.105622_bib0010) 2015 |
| References_xml | – start-page: 886 year: 2005 end-page: 893 ident: bib0013 article-title: Histograms of oriented gradients for human detection publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – reference: . Last accessed: 10/27/2019. – start-page: 1 year: 2018 end-page: 6 ident: bib0001 article-title: Effect of averaging techniques on PCA algorithm and its performance evaluation in face recognition applications publication-title: International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) – start-page: 1 year: 2018 end-page: 4 ident: bib0006 article-title: HOG-CNN based real time face recognition publication-title: International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE) – start-page: 1 year: 2015 end-page: 9 ident: bib0009 article-title: Going deeper with convolutions publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – start-page: 770 year: 2016 end-page: 778 ident: bib0007 article-title: Deep residual learning for image recognition publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 24 start-page: 971 year: 2002 end-page: 987 ident: bib0008 article-title: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 448 year: 2015 end-page: 456 ident: bib0010 article-title: Batch normalization accelerating deep network training by reducing internal covariate shift publication-title: International Conference on Machine Learning (ICML) – year: 2014 ident: bib0002 article-title: Face recognition methods & applications publication-title: CoRR abs/1403.0485 – start-page: 1606 year: 2017 end-page: 1610 ident: bib0004 article-title: Face recognition based on multi-direction local binary pattern publication-title: 3rd IEEE International Conference on Computer and Communications (ICCC) – start-page: 1368 year: 2017 end-page: 1372 ident: bib0005 article-title: Deep convolutional neural network applies to face recognition in small and medium databases publication-title: 4th International Conference on Systems and Informatics (ICSAI) – reference: CBCL pedestrian database, website: – volume: 28 start-page: 2037 year: 2006 end-page: 2041 ident: bib0003 article-title: Face Description with Local Binary Patterns: application to Face Recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 2818 year: 2016 end-page: 2826 ident: bib0011 article-title: Rethinking the inception architecture for computer vision publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – year: 2014 ident: 10.1016/j.cmpb.2020.105622_bib0002 article-title: Face recognition methods & applications publication-title: CoRR abs/1403.0485 – volume: 28 start-page: 2037 issue: 12 year: 2006 ident: 10.1016/j.cmpb.2020.105622_bib0003 article-title: Face Description with Local Binary Patterns: application to Face Recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2006.244 – start-page: 770 year: 2016 ident: 10.1016/j.cmpb.2020.105622_bib0007 article-title: Deep residual learning for image recognition – start-page: 1 year: 2018 ident: 10.1016/j.cmpb.2020.105622_bib0001 article-title: Effect of averaging techniques on PCA algorithm and its performance evaluation in face recognition applications – start-page: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105622_bib0009 article-title: Going deeper with convolutions – start-page: 448 year: 2015 ident: 10.1016/j.cmpb.2020.105622_bib0010 article-title: Batch normalization accelerating deep network training by reducing internal covariate shift – start-page: 1606 year: 2017 ident: 10.1016/j.cmpb.2020.105622_bib0004 article-title: Face recognition based on multi-direction local binary pattern – start-page: 2818 year: 2016 ident: 10.1016/j.cmpb.2020.105622_bib0011 article-title: Rethinking the inception architecture for computer vision – ident: 10.1016/j.cmpb.2020.105622_bib0012 – start-page: 1 year: 2018 ident: 10.1016/j.cmpb.2020.105622_bib0006 article-title: HOG-CNN based real time face recognition – start-page: 1368 year: 2017 ident: 10.1016/j.cmpb.2020.105622_bib0005 article-title: Deep convolutional neural network applies to face recognition in small and medium databases – start-page: 886 year: 2005 ident: 10.1016/j.cmpb.2020.105622_bib0013 article-title: Histograms of oriented gradients for human detection – volume: 24 start-page: 971 issue: 7 year: 2002 ident: 10.1016/j.cmpb.2020.105622_bib0008 article-title: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2002.1017623 |
| SSID | ssj0002556 |
| Score | 2.448742 |
| Snippet | •Face recognition success rate is limited by many environmental factors.•Low generalization ability of a single convolutional neural network is... Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 105622 |
| SubjectTerms | Algorithms Convolutional Neural Networks (CNN) Ensemble learning Face - diagnostic imaging Face recognition Facial Recognition Local Binary Patterns (LBP) Machine Learning Neural Networks, Computer |
| Title | Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0169260720314553 https://dx.doi.org/10.1016/j.cmpb.2020.105622 https://www.ncbi.nlm.nih.gov/pubmed/32629293 https://www.proquest.com/docview/2421110418 |
| Volume | 197 |
| WOSCitedRecordID | wos000594824200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7565 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002556 issn: 0169-2607 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9swFBZpOsZeyu5LtxUN9hZcYl0s-bEbLVthpSMZ5M3YlrwmJE7Ijfz8Hd28JaXdBfZiG2Fd0PksnSOf8x2E3pNexaTKFXxIUkVMpiQqhIwjUlWUcVChpZY22YS4upLDYXrdam1DLMxmIupabrfp_L-KGspA2CZ09i_E3TQKBfAMQocriB2ufyT463xh8qNMumCg6qkJjJqE4w_rQl5vfPcgHMNmaW_WF9zRNdvdrVu4ON25pd-sLWmDyc1jkqx4jyMvz8By4LND-JTUS09AYJ2_rMuti_Pf-Y8_8GfVlzApowak_bUp-zqy2UW-75Z-gNKbdbOTXHh_4v5o6kfjzy_Ivi_I7cAad86ZpBGYWmJnoXaevLcWfXf-MD4tp_Pi1HRhkhcnLtx5j0y7bxo27RJD2885PUCHRPBUttHh2efz4WWzixtqNscL7wbiA66cb-B-T3cpNXcZLVZ5GTxGR97qwGcOLU9QS9dP0cMvXh7P0DiABgfQ4AAaPKvwDmiwAw0OoMEgaWxBgx1ocAANBtBgAxr8C2ieo28X54OPnyKfhSMqmaCrSElakURXhFOuElEwRjTokbrkIqY6KfJYxUJRlReViFkcFzwnGux4oz1qKhV9gdr1rNavEAZjmWqpVdkrKKOVTCXNS6mg5ZyphPU6KA7TmJWeot5kSplkwRdxnJmpz8zUZ27qO6jb1Jk7gpZ736ZBOlkIPYbNMgMo3VuLN7W8YuoUzt_WexcAkMGqbX7F5bWerZeZccQAxZvFsoNeOmQ0oweDioDRQo__aayv0aOfX9kb1F4t1votelBuVqPl4gQdiKE88Vj_AWJSyVE |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Parallel+ensemble+learning+of+convolutional+neural+networks+and+local+binary+patterns+for+face+recognition&rft.jtitle=Computer+methods+and+programs+in+biomedicine&rft.au=Tang%2C+Jialin&rft.au=Su%2C+Qinglang&rft.au=Su%2C+Binghua&rft.au=Fong%2C+Simon&rft.date=2020-12-01&rft.pub=Elsevier+B.V&rft.issn=0169-2607&rft.volume=197&rft_id=info:doi/10.1016%2Fj.cmpb.2020.105622&rft.externalDocID=S0169260720314553 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0169-2607&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0169-2607&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0169-2607&client=summon |