  Facial expression recognition (FER) has emerged as an important component of
human-computer interaction systems. Despite recent advancements in FER,
performance often drops significantly for non-frontal facial images. We propose
Contrastive Learning of Multi-view facial Expressions (CL-MEx) to exploit
facial images captured simultaneously from different angles towards FER. CL-MEx
is a two-step training framework. In the first step, an encoder network is
pre-trained with the proposed self-supervised contrastive loss, where it learns
to generate view-invariant embeddings for different views of a subject. The
model is then fine-tuned with labeled data in a supervised setting. We
demonstrate the performance of the proposed method on two multi-view FER
datasets, KDEF and DDCF, where state-of-the-art performances are achieved.
Further experiments show the robustness of our method in dealing with
challenging angles and reduced amounts of labeled data.
