  Current state-of-the-art models for automatic FER are based on very deep
neural networks that are difficult to train. This makes it challenging to adapt
these models to changing conditions, a requirement from FER models given the
subjective nature of affect perception and understanding. In this paper, we
address this problem by formalizing the FaceChannel, a light-weight neural
network that has much fewer parameters than common deep neural networks. We
perform a series of experiments on different benchmark datasets to demonstrate
how the FaceChannel achieves a comparable, if not better, performance, as
compared to the current state-of-the-art in FER.
