  Facial expression recognition (FER) models are typically trained on datasets
with a fixed number of seven basic classes. However, recent research works
point out that there are far more expressions than the basic ones. Thus, when
these models are deployed in the real world, they may encounter unknown
classes, such as compound expressions that cannot be classified into existing
basic classes. To address this issue, we propose the open-set FER task for the
first time. Though there are many existing open-set recognition methods, we
argue that they do not work well for open-set FER because FER data are all
human faces with very small inter-class distances, which makes the open-set
samples very similar to close-set samples. In this paper, we are the first to
transform the disadvantage of small inter-class distance into an advantage by
proposing a new way for open-set FER. Specifically, we find that small
inter-class distance allows for sparsely distributed pseudo labels of open-set
samples, which can be viewed as symmetric noisy labels. Based on this novel
observation, we convert the open-set FER to a noisy label detection problem. We
further propose a novel method that incorporates attention map consistency and
cycle training to detect the open-set samples. Extensive experiments on various
FER datasets demonstrate that our method clearly outperforms state-of-the-art
open-set recognition methods by large margins. Code is available at
https://github.com/zyh-uaiaaaa.
