  In domains where computational resources and labeled data are limited, such
as in robotics, deep networks with millions of weights might not be the optimal
solution. In this paper, we introduce a connectivity scheme for pyramidal
architectures to increase their capacity for learning features. Experiments on
facial expression recognition of unseen people demonstrate that our approach is
a potential candidate for applications with restricted resources, due to good
generalization performance and low computational cost. We show that our
approach generalizes as well as convolutional architectures in this task but
uses fewer trainable parameters and is more robust for low-resolution faces.
