  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.
