  Human emotions can be inferred from facial expressions. However, the
annotations of facial expressions are often highly noisy in common emotion
coding models, including categorical and dimensional ones. To reduce human
labelling effort on multi-task labels, we introduce a new problem of facial
emotion recognition with noisy multi-task annotations. For this new problem, we
suggest a formulation from the point of joint distribution match view, which
aims at learning more reliable correlations among raw facial images and
multi-task labels, resulting in the reduction of noise influence. In our
formulation, we exploit a new method to enable the emotion prediction and the
joint distribution learning in a unified adversarial learning game. Evaluation
throughout extensive experiments studies the real setups of the suggested new
problem, as well as the clear superiority of the proposed method over the
state-of-the-art competing methods on either the synthetic noisy labeled
CIFAR-10 or practical noisy multi-task labeled RAF and AffectNet. The code is
available at https://github.com/sanweiliti/noisyFER.
