Authentic Emotion Detection in Real-Time Video

There is a growing trend toward emotional intelligence in human-computer interaction paradigms. In order to react appropriately to a human, the computer would need to have some perception of the emotional state of the human. We assert that the most informative channel for machine perception of emotions is through facial expressions in video. One current difficulty in evaluating automatic emotion detection is that there are currently no international databases which are based on authentic emotions. The current facial expression databases contain facial expressions which are not naturally linked to the emotional state of the test subject. Our contributions in this work are twofold: First, we create the first authentic facial expression database where the test subjects are showing the natural facial expressions based upon their emotional state. Second, we evaluate the several promising machine learning algorithms for emotion detection which include techniques such as Bayesian Networks, SVMs, and Decision trees.

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