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

arXiv: https://arxiv.org/abs/2103.12362 · HTML: https://mlsanity.com/paper/2103.12362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?](https://mlsanity.com/paper/1510.02969/) (2015)

## Abstract (arXiv)

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.