Automated understanding of facial expressions is a fundamental step towards high-level human-computer interaction. The goal of this proposal is to develop a solution to this problem by taking advantage of the color, depth and temporal information provided by an RGB-D video feed. We plan to model human facial expressions through the analysis of temporal variations in the pattern of activations of their natural constituents, three-dimensional Action Units. As starting point for algorithm development, we propose to build on our prior experience developing convolutional neural network architectures for fine-grained localization, RGB-D scene understanding and video analysis.
@InProceedings{CLEI-2015:GoogleCharla1,
author = {Pablo Arbelaez},
title = {Learning Dynamic Action Units for Three-dimensional Facial Expression Recognition},
booktitle = {2015 XLI Latin American Computing Conference (CLEI), Special Edition},
pages = {188--188},
year = {2015},
editor = {Universidad Católica San Pablo},
address = {Arequipa-Peru},
month = {October},
organization = {CLEI},
publisher = {CLEI},
url = {http://clei.org/clei2015/GoogleCharla1},
isbn = {978-9972-825-91-0},
}