  The ability to recognize facial expressions automatically enables novel
applications in human-computer interaction and other areas. Consequently, there
has been active research in this field, with several recent works utilizing
Convolutional Neural Networks (CNNs) for feature extraction and inference.
These works differ significantly in terms of CNN architectures and other
factors. Based on the reported results alone, the performance impact of these
factors is unclear. In this paper, we review the state of the art in
image-based facial expression recognition using CNNs and highlight algorithmic
differences and their performance impact. On this basis, we identify existing
bottlenecks and consequently directions for advancing this research field.
Furthermore, we demonstrate that overcoming one of these bottlenecks - the
comparatively basic architectures of the CNNs utilized in this field - leads to
a substantial performance increase. By forming an ensemble of modern deep CNNs,
we obtain a FER2013 test accuracy of 75.2%, outperforming previous works
without requiring auxiliary training data or face registration.
