Neural nets model audience reactions to movies

Disney Research used deep learning methods to develop a new means of assessing complex audience reactions to movies via facial expressions and demonstrated that the new technique outperformed conventional methods. The new method, called factorized variational autoencoders or FVAEs demonstrated a surprising ability to reliably predict a viewer's facial expressions for the remainder of the movie after observing an audience member for only a few minutes. While the experimental results are still preliminary, this approach demonstrates tremendous promise to more accurately model group facial expressions in a wide range of applications. "The FVAEs were able to learn concepts such as smiling and laughing on their own," said Zhiwei Deng, a Ph.D. student at Simon Fraser University who served as a lab associate at Disney Research. "What's more, they were able to show how these facial expressions correlated with humorous scenes." The researchers will present their findings at the IEEE Conference on Computer Vision and Pattern Recognition on July 22 in Honolulu.