The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
Abstract
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.
Cite
@article{arxiv.2004.08195,
title = {The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition},
author = {Pablo Barros and Nikhil Churamani and Alessandra Sciutti},
journal= {arXiv preprint arXiv:2004.08195},
year = {2020}
}
Comments
Accepted at the Workshop on Affect Recognition in-the-wild: Uni/Multi-Modal Analysis & VA-AU-Expression Challenges, FG2020