The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to other published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these features tracks the facial expressions of a player in real-time.
@article{arxiv.1408.3750,
title = {Real-time emotion recognition for gaming using deep convolutional network features},
author = {Sébastien Ouellet},
journal= {arXiv preprint arXiv:1408.3750},
year = {2014}
}