  As various databases of facial expressions have been made accessible over the
last few decades, the Facial Expression Recognition (FER) task has gotten a lot
of interest. The multiple sources of the available databases raised several
challenges for facial recognition task. These challenges are usually addressed
by Convolution Neural Network (CNN) architectures. Different from CNN models, a
Transformer model based on attention mechanism has been presented recently to
address vision tasks. One of the major issue with Transformers is the need of a
large data for training, while most FER databases are limited compared to other
vision applications. Therefore, we propose in this paper to learn a vision
Transformer jointly with a Squeeze and Excitation (SE) block for FER task. The
proposed method is evaluated on different publicly available FER databases
including CK+, JAFFE,RAF-DB and SFEW. Experiments demonstrate that our model
outperforms state-of-the-art methods on CK+ and SFEW and achieves competitive
results on JAFFE and RAF-DB.
