  Facial expression recognition (FER) remains a challenging task due to the
ambiguity of expressions. The derived noisy labels significantly harm the
performance in real-world scenarios. To address this issue, we present a new
FER model named Landmark-Aware Net~(LA-Net), which leverages facial landmarks
to mitigate the impact of label noise from two perspectives. Firstly, LA-Net
uses landmark information to suppress the uncertainty in expression space and
constructs the label distribution of each sample by neighborhood aggregation,
which in turn improves the quality of training supervision. Secondly, the model
incorporates landmark information into expression representations using the
devised expression-landmark contrastive loss. The enhanced expression feature
extractor can be less susceptible to label noise. Our method can be integrated
with any deep neural network for better training supervision without
introducing extra inference costs. We conduct extensive experiments on both
in-the-wild datasets and synthetic noisy datasets and demonstrate that LA-Net
achieves state-of-the-art performance.
