  Affective computing and cognitive theory are widely used in modern
human-computer interaction scenarios. Human faces, as the most prominent and
easily accessible features, have attracted great attention from researchers.
Since humans have rich emotions and developed musculature, there exist a lot of
fine-grained expressions in real-world applications. However, it is extremely
time-consuming to collect and annotate a large number of facial images, of
which may even require psychologists to correctly categorize them. To the best
of our knowledge, the existing expression datasets are only limited to several
basic facial expressions, which are not sufficient to support our ambitions in
developing successful human-computer interaction systems. To this end, a novel
Fine-grained Facial Expression Database - F2ED is contributed in this paper,
and it includes more than 200k images with 54 facial expressions from 119
persons. Considering the phenomenon of uneven data distribution and lack of
samples is common in real-world scenarios, we further evaluate several tasks of
few-shot expression learning by virtue of our F2ED, which are to recognize the
facial expressions given only few training instances. These tasks mimic human
performance to learn robust and general representation from few examples. To
address such few-shot tasks, we propose a unified task-driven framework -
Compositional Generative Adversarial Network (Comp-GAN) learning to synthesize
facial images and thus augmenting the instances of few-shot expression classes.
Extensive experiments are conducted on F2ED and existing facial expression
datasets, i.e., JAFFE and FER2013, to validate the efficacy of our F2ED in
pre-training facial expression recognition network and the effectiveness of our
proposed approach Comp-GAN to improve the performance of few-shot recognition
tasks.
