Real-time emotion recognition for gaming using deep convolutional network features
This addresses emotion recognition for gaming applications, but it is incremental as it uses existing deep features with similar performance to prior work.
The study tackled real-time emotion recognition for gaming by applying deep convolutional network features to single still images, achieving a best recognition rate of 94.4%, similar to other models, and implemented an affective feedback game for real-time tracking.
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.