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}, }