Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neural networks was backed by the availability of numerous labeled datasets. These models, most of the time, present state-of-the-art results in such benchmarks, but are very difficult to adapt to other scenarios. In this paper, we present one more chapter of benchmarking different versions of the FaceChannel neural network: we demonstrate how our little model can predict affective information from the facial expression on the novel AffWild2 dataset.
@article{arxiv.2010.01557,
title = {The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge},
author = {Pablo Barros and Alessandra Sciutti},
journal= {arXiv preprint arXiv:2010.01557},
year = {2020}
}
Comments
Submission that describes the models submitted to the AffWild2 challenge