Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

TL;DR

This paper introduces a novel Consensual Collaborative Training framework for facial expression recognition that effectively handles noisy labels by co-training multiple networks and using knowledge distillation, achieving state-of-the-art results.

Contribution

The work proposes a noise-robust training strategy for FER that does not assume noise distribution and uses a dynamic transition from supervision to consensus loss.

Findings

Effective on synthetic and real noisy FER datasets.

Achieves state-of-the-art accuracy on benchmark FER datasets.

Validates on a large crowd-annotated dataset.

Abstract

Presence of noise in the labels of large scale facial expression datasets has been a key challenge towards Facial Expression Recognition (FER) in the wild. 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 work proposes an effective training strategy in the presence of noisy labels, called as Consensual Collaborative Training (CCT) framework. 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. Inference is done using a single network based on a simple knowledge distillation scheme. Effectiveness of the proposed framework is demonstrated on synthetic as well as real noisy FER datasets. In addition, a large test subset of around 5K images is annotated from the FEC dataset using crowd wisdom of 16 different annotators and reliable labels are inferred. CCT is also validated on it. State-of-the-art performance is reported on the benchmark FER datasets RAFDB (90.84%) FERPlus (89.99%) and AffectNet (66%). Our codes are available at https://github.com/1980x/CCT.