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...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 197; p. 105622
Main Authors:	Tang, Jialin, Su, Qinglang, Su, Binghua, Fong, Simon, Cao, Wei, Gong, Xueyuan
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01.12.2020
Subjects:	Algorithms Convolutional Neural Networks (CNN) Ensemble learning Face - diagnostic imaging Face recognition Facial Recognition Local Binary Patterns (LBP) Machine Learning Neural Networks, Computer Local Binary Patterns (LBP) Convolutional Neural Networks (CNN) Face recognition Ensemble learning
ISSN:	0169-2607, 1872-7565, 1872-7565
Online Access:	Get full text
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Summary:	•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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0169-2607 1872-7565 1872-7565
DOI:	10.1016/j.cmpb.2020.105622