  Facial emotion recognition (FER) is significant for human-computer
interaction such as clinical practice and behavioral description. Accurate and
robust FER by computer models remains challenging due to the heterogeneity of
human faces and variations in images such as different facial pose and
lighting. Among all techniques for FER, deep learning models, especially
Convolutional Neural Networks (CNNs) have shown great potential due to their
powerful automatic feature extraction and computational efficiency. In this
work, we achieve the highest single-network classification accuracy on the
FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its
hyperparameters, and experiment with various optimization methods. To our best
knowledge, our model achieves state-of-the-art single-network accuracy of 73.28
% on FER2013 without using extra training data.
