A Comparative Study of Machine Learning Techniques for Emotion Recognition

Humans share emotions which they exhibit through facial expressions. Automatic human emotion recognition algorithm in images and videos aims at detection, extraction, and evaluation of these facial expressions. This paper provides a comparison between various multi-class prediction algorithms employed on the Cohn-Kanade dataset (Lucey in The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression, pp. 94–101, 2010 [1]). The different machine learning algorithms can be used to provide emotion recognition task. We have compared the performance of K-nearest neighbors, Support Vector Machine, and neural network.

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