Deep learning has played a significant role in the success of facial
expression recognition (FER), thanks to large models and vast amounts of
labelled data. However, obtaining labelled data requires a tremendous amount of
human effort, time, and financial resources. Even though some prior works have
focused on reducing the need for large amounts of labelled data using different
unsupervised methods, another promising approach called active learning is
barely explored in the context of FER. This approach involves selecting and
labelling the most representative samples from an unlabelled set to make the
best use of a limited 'labelling budget'. In this paper, we implement and study
8 recent active learning methods on three public FER datasets, FER13, RAF-DB,
and KDEF. Our findings show that existing active learning methods do not
perform well in the context of FER, likely suffering from a phenomenon called
'Cold Start', which occurs when the initial set of labelled samples is not well
representative of the entire dataset. To address this issue, we propose
contrastive self-supervised pre-training, which first learns the underlying
representations based on the entire unlabelled dataset. We then follow this
with the active learning methods and observe that our 2-step approach shows up
to 9.2% improvement over random sampling and up to 6.7% improvement over the
best existing active learning baseline without the pre-training. We will make
the code for this study public upon publication at:
github.com/ShuvenduRoy/ActiveFER.

深度学习在面部表情识别方面发挥了重要作用，但标记数据获取需要巨大人力和财力投入，研究发现已有的主动学习方法在面部表情识别上表现不佳，为解决该问题，提出了对比自监督预训练方法，通过两步骤的方法，相较于随机采样提高了 9.2% 的准确度。