Real-time emotion recognition for gaming using deep convolutional network features
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 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.
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