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

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.