  Unlike the constraint frontal face condition, faces in the wild have various
unconstrained interference factors, such as complex illumination, changing
perspective and various occlusions. Facial expressions recognition (FER) in the
wild is a challenging task and existing methods can't perform well. However,
for occluded faces (containing occlusion caused by other objects and
self-occlusion caused by head posture changes), the attention mechanism has the
ability to focus on the non-occluded regions automatically. In this paper, we
propose a Lossless Attention Model (LLAM) for convolutional neural networks
(CNN) to extract attention-aware features from faces. Our module avoids decay
information in the process of generating attention maps by using the
information of the previous layer and not reducing the dimensionality.
Sequentially, we adaptively refine the feature responses by fusing the
attention map with the feature map. We participate in the seven basic
expression classification sub-challenges of FG-2020 Affective Behavior Analysis
in-the-wild Challenge. And we validate our method on the Aff-Wild2 datasets
released by the Challenge. The total accuracy (Accuracy) and the unweighted
mean (F1) of our method on the validation set are 0.49 and 0.38 respectively,
and the final result is 0.42 (0.67 F1-Score + 0.33 Accuracy).
