  This paper introduces BReG-NeXt, a residual-based network architecture using
a function wtih bounded derivative instead of a simple shortcut path (a.k.a.
identity mapping) in the residual units for automatic recognition of facial
expressions based on the categorical and dimensional models of affect. Compared
to ResNet, our proposed adaptive complex mapping results in a shallower network
with less numbers of training parameters and floating point operations per
second (FLOPs). Adding trainable parameters to the bypass function further
improves fitting and training the network and hence recognizing subtle facial
expressions such as contempt with a higher accuracy. We conducted comprehensive
experiments on the categorical and dimensional models of affect on the
challenging in-the-wild databases of AffectNet, FER2013, and Affect-in-Wild.
Our experimental results show that our adaptive complex mapping approach
outperforms the original ResNet consisting of a simple identity mapping as well
as other state-of-the-art methods for Facial Expression Recognition (FER).
Various metrics are reported in both affect models to provide a comprehensive
evaluation of our method. In the categorical model, BReG-NeXt-50 with only 3.1M
training parameters and 15 MFLOPs, achieves 68.50% and 71.53% accuracy on
AffectNet and FER2013 databases, respectively. In the dimensional model,
BReG-NeXt achieves 0.2577 and 0.2882 RMSE value on AffectNet and Affect-in-Wild
databases, respectively.
