Emotion expression is a type of nonverbal communication (i.e. wordless cues) between people, where affect plays the role of interpersonal communication with information conveyed by facial and/or body expressions. Much can be understood about how people are feeling through their expressions, which are crucial for everyday communication and interaction. This paper presents a study on spatiotemporal feature extraction based on tracked facial landmarks. The features are tested with multiple classification methods to verify whether they are discriminative enough for an automatic emotion recognition system. The Karolinska Directed Emotional Faces (KDEF)  were used to determine features representing the human facial expressions of angry, disgusted, happy, sad, afraid, surprised and neutral. The resulting set of features were tested using K-fold cross-validation. Experimental results show that facial expressions can be recognised correctly with an accuracy of up to 87% when using the newly-developed features and a multiclass Support Vector Machine classifier.
2018 International Conference on Intelligent Systems (IS), IEEE