A semi-supervised temporal clustering method for facial emotion analysis
Rodrigo Araujo, Mohamed S. Kamel · 2014
In this paper, we propose a semi-supervised temporal clustering method and apply it to the complex problem of facial emotion categorization. The proposed method, which uses a mechanism to add side information based on the semi-supervised kernel k-means framework, is an extension of the temporal clustering algorithm Aligned Cluster Analysis (ACA). We show that simply adding a small amount of soft constraints, in the form of must-link and cannot-link, improves the overall accuracy of the state-of-the-art method, ACA without adding any extra computational complexity. The results on the non-posed database VAM corpus for three different emotion primitives (valence, dominance, and activation) show improvements compared to the original approach.