  Facial Expression Recognition (FER) is crucial in many research domains
because it enables machines to better understand human behaviours. FER methods
face the problems of relatively small datasets and noisy data that don't allow
classical networks to generalize well. To alleviate these issues, we guide the
model to concentrate on specific facial areas like the eyes, the mouth or the
eyebrows, which we argue are decisive to recognise facial expressions. We
propose the Privileged Attribution Loss (PAL), a method that directs the
attention of the model towards the most salient facial regions by encouraging
its attribution maps to correspond to a heatmap formed by facial landmarks.
Furthermore, we introduce several channel strategies that allow the model to
have more degrees of freedom. The proposed method is independent of the
backbone architecture and doesn't need additional semantic information at test
time. Finally, experimental results show that the proposed PAL method
outperforms current state-of-the-art methods on both RAF-DB and AffectNet.
