# The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2004.08195 · HTML: https://mlsanity.com/paper/2004.08195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Robust Neonatal Face Detection in Real-world Clinical Settings](https://mlsanity.com/paper/2204.00655/) (2022)
- [Multi-threshold Deep Metric Learning for Facial Expression Recognition](https://mlsanity.com/paper/2406.16434/) (2024)
- [I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition](https://mlsanity.com/paper/2210.17421/) (2022)

## Abstract (arXiv)

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.