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Facial expression recognition (FER) remains a challenging task due to theambiguity of expressions. The derived noisy labels significantly harm theperformance in real-world scenarios. To address this issue, we present a newFER model named Landmark-Aware Net~(LA-Net), which leverages facial landmarksto mitigate the impact of label noise from two perspectives. Firstly, LA-Netuses landmark information to suppress the uncertainty in expression space andconstructs the label distribution of each sample by neighborhood aggregation,which in turn improves the quality of training supervision. Secondly, the modelincorporates landmark information into expression representations using thedevised expression-landmark contrastive loss. The enhanced expression featureextractor can be less susceptible to label noise. Our method can be integratedwith any deep neural network for better training supervision withoutintroducing extra inference costs. We conduct extensive experiments on bothin-the-wild datasets and synthetic noisy datasets and demonstrate that LA-Netachieves state-of-the-art performance.

LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise