FER‐Net: facial expression recognition using densely connected convolutional network

Abstract

Convolutional neural network (CNN) architectures have shown excellent image classification performance on large-scale visual recognition tasks. If a CNN architecture contains a shorter connection between layers close to the input and those close to the output, the training can be deeper, more accurate and efficient. In this Lette, the authors propose a densely connected CNN architecture for facial expression recognition (FER-Net), which connects the output of each convolution layer to the inputs of the next convolution layers in the architecture. Experiments conducted on a publicly available dataset show that FER-Net produces state-of-the-art results in facial expression recognition.