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

A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition