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- W3048798720 abstract "In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising." @default.
- W3048798720 created "2020-08-18" @default.
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- W3048798720 date "2020-07-01" @default.
- W3048798720 modified "2023-10-16" @default.
- W3048798720 title "Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network" @default.
- W3048798720 cites W2156387975 @default.
- W3048798720 doi "https://doi.org/10.4018/ijeach.2020070104" @default.
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