Nesterov accelerated gradient descent-based convolution neural network with dropout for facial expression recognition

Abstract

Nesterov accelerated gradient descent-based convolution neural network (NAGDCNN) with dropout is proposed for facial expression recognition, which fuses the convolution neural network (CNN) with Softmax regression to construct a deep convolution neural network (DCNN) that can excavate high-level expression features and classify them. The dropout layer is added after the sub-sampling layer which can effectively reduce overfitting and the network's training time, moreover, the Nesterov accelerated gradient descent (NAGD) is used to optimize the network weights that can predictably prevent the algorithm from going too fast or too slow and enhance the response capability of the network. To verify the effectiveness of the proposal, experiments on benchmark database are conducted, and the experimental results show that the proposal outperforms the state-of-the-art methods. Futhermore, the application experiment is also curried out and the results indicate the feasibility of the proposal in practical applications.