  Although there has been much progress in the area of facial expression
recognition (FER), most existing methods suffer when presented with images that
have been captured from viewing angles that are non-frontal and substantially
different from those used in the training process. In this paper, we propose
ViewFX, a novel view-invariant FER framework based on contrastive learning,
capable of accurately classifying facial expressions regardless of the input
viewing angles during inference. ViewFX learns view-invariant features of
expression using a proposed self-supervised contrastive loss which brings
together different views of the same subject with a particular expression in
the embedding space. We also introduce a supervised contrastive loss to push
the learnt view-invariant features of each expression away from other
expressions. Since facial expressions are often distinguished with very subtle
differences in the learned feature space, we incorporate the Barlow twins loss
to reduce the redundancy and correlations of the representations in the learned
representations. The proposed method is a substantial extension of our
previously proposed CL-MEx, which only had a self-supervised loss. We test the
proposed framework on two public multi-view facial expression recognition
datasets, KDEF and DDCF. The experiments demonstrate that our approach
outperforms previous works in the area and sets a new state-of-the-art for both
datasets while showing considerably less sensitivity to challenging angles and
the number of output labels used for training. We also perform detailed
sensitivity and ablation experiments to evaluate the impact of different
components of our model as well as its sensitivity to different parameters.
