The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition

TL;DR

The FaceChannel is a lightweight neural network designed for facial expression recognition that achieves comparable or better performance than deep models while being faster and less resource-intensive.

Contribution

We introduce the FaceChannel, a novel, efficient neural network with an inhibitory layer that improves FER performance with fewer parameters.

Findings

FaceChannel achieves state-of-the-art results on benchmark datasets.

The model performs well across different datasets in cross-dataset analysis.

FaceChannel requires less training time and computational resources.

Abstract

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. 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 introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and thus improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapt the learned facial features towards the different datasets.