  Facial expression recognition (FER) in the wild is crucial for building
reliable human-computer interactive systems. However, annotations of large
scale datasets in FER has been a key challenge as these datasets suffer from
noise due to various factors like crowd sourcing, subjectivity of annotators,
poor quality of images, automatic labelling based on key word search etc. Such
noisy annotations impede the performance of FER due to the memorization ability
of deep networks. During early learning stage, deep networks fit on clean data.
Then, eventually, they start overfitting on noisy labels due to their
memorization ability, which limits FER performance. This report presents
Consensual Collaborative Training (CCT) framework used in our submission to
expression recognition track of the Affective Behaviour Analysis in-the-wild
(ABAW) 2021 competition. CCT co-trains three networks jointly using a convex
combination of supervision loss and consistency loss, without making any
assumption about the noise distribution. A dynamic transition mechanism is used
to move from supervision loss in early learning to consistency loss for
consensus of predictions among networks in the later stage. Co-training reduces
overall error, and consistency loss prevents overfitting to noisy samples. The
performance of the model is validated on challenging Aff-Wild2 dataset for
categorical expression classification. Our code is made publicly available at
https://github.com/1980x/ABAW2021DMACS.
