基于深度残差网络的人脸表情识别

针对深度卷积神经网络随着卷积层数增加而导致网络模型难以训练和性能退化等问题，提出了一种基于深度残差网络的人脸表情识别方法。该方法利用残差学习单元来改善深度卷积神经网络模型训练寻优的过程，减少模型收敛的时间开销。此外，为了提高网络模型的泛化能力，从KDEF和CK+两种表情数据集上选取表情图像样本组成混合数据集用以训练网络。在混合数据集上采用十折（10-fold）交叉验证方法进行了实验，比较了不同深度的带有残差学习单元的残差网络与不带残差学习单元的常规卷积神经网络的表情识别准确率。当采用74层的深度残差网络时，可以获得90.79%的平均识别准确率。实验结果表明采用残差学习单元构建的深度残差网络可以解决网络深度和模型收敛性之间的矛盾，并能提升表情识别的准确率。

Facial Expression Recognition Based on Deep Residual Network

The training of deep convolutional neural networks becomes more and more difficult and its performance is degraded with the increase of the number of convolution layers to solve the problem. A facial expression recognition method is presented based on deep residual network. The method uses building blocks for residual learning to improve the training and optimization process of the deep convolutional neural network model and reduce the time cost of the model convergence. In addition, to improve the generalization ability of the network model, a hybrid dataset for training network model is made up of the expression image samples which are selected from the KDEF and CK+ expression datasets. The comparative experiment was conducted with 10-fold cross validation method on the hybrid dataset. In term of expression recognition accuracy, we compared the residual networks with residual learning and the conventional convolution neural networks without residual learning and demonstrated the effect of network depth on the recognition accuracy. The average recognition accuracy of 90.79% is achieved as a 74-layer deep residual network is adopted. The experimental results show that the deep convolutional neural network constructed with building blocks for residual learning can solve the contradiction between the network depth and the model convergence, and can improve the accuracy of expression recognition.