Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition

Traditional deep convolutional neural networks are used for facial expression recognition, which makes the number of neurons and parameters huge, wastes computing resources, and even causes problems such as overfitting and network degradation. Meanwhile, single-scale expression features cannot describe rich facial expression information and are not evenly distributed on face images. We proposed an improved Xception method based on the original Xception network, it retains the depth-separable convolution and residual structure, adds multi-scale convolution and local dense connection. Our proposed method achieved an accuracy rate of 89.54% on the FER2013 dataset, strengthened feature reuse, and enhanced generalization ability.