  Facial expression is one of the most powerful, natural, and universal signals
for human beings to express emotional states and intentions. Thus, it is
evident the importance of correct and innovative facial expression recognition
(FER) approaches in Artificial Intelligence. The current common practice for
FER is to correctly design convolutional neural networks' architectures (CNNs)
using human expertise. However, finding a well-performing architecture is often
a very tedious and error-prone process for deep learning researchers. Neural
architecture search (NAS) is an area of growing interest as demonstrated by the
large number of scientific works published in recent years thanks to the
impressive results achieved in recent years. We propose a genetic algorithm
approach that uses an ingenious encoding-decoding mechanism that allows to
automatically evolve CNNs on FER tasks attaining high accuracy classification
rates. The experimental results demonstrate that the proposed algorithm
achieves the best-known results on the CK+ and FERG datasets as well as
competitive results on the JAFFE dataset.
