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Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition
Jialin Tang1,2; Qinglang Su2; Binghua Su1; Simon Fong3; Wei Cao1; Xueyuan Gong1
2020-12-01
Source PublicationComputer Methods and Programs in Biomedicine
ISSN0169-2607
Volume197
Abstract

Background and Objective: 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.

Methods: 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.

Results: 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%.

Conclusion: In summary, the proposed approach greatly outperforms other competitive methods.

KeywordConvolutional Neural Networks (Cnn) Local Binary Patterns (Lbp) Ensemble Learning Face Recognition
DOI10.1016/j.cmpb.2020.105622
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering ; Medical Informatics
WOS SubjectComputer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS IDWOS:000594824200002
PublisherELSEVIER IRELAND LTD, ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND
Scopus ID2-s2.0-85087328894
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorSimon Fong; Xueyuan Gong
Affiliation1.Beijing Institute of Technology,Zhuhai,519088,China
2.City University of Macau,China
3.Department of Computer and Information Science,University of Macau,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jialin Tang,Qinglang Su,Binghua Su,et al. Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition[J]. Computer Methods and Programs in Biomedicine,2020,197.
APA Jialin Tang,Qinglang Su,Binghua Su,Simon Fong,Wei Cao,&Xueyuan Gong.(2020).Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.Computer Methods and Programs in Biomedicine,197.
MLA Jialin Tang,et al."Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition".Computer Methods and Programs in Biomedicine 197(2020).
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