Using a sparse learning relevance vector machine in facial expression recognition

At TUDelft there is a project aiming at the realization of' a fully automatic emotion recognition system on the basis of' facial analysis. The exploited approach splits the system into four components. Face detection, facial characteristic point extraction, tracking and classification. The focus in this paper will only be on the first two components. Face detection is employed by boosting simple rectangle Haar-like features that give a decent representation of the face. These features also allow the differentiation between a face and a non-face. The boosting algorithm is combined with an Evolutionary Search to speed up the overall search time. Facial characteristic points (FCP) are extracted from the detected faces. The same technique applied on faces is utilized for this purpose. Additionally, FCP extraction using corner detection methods and brightness distribution has also been considered. Finally, after retrieving the required FCPs the emotion of the facial expression can be determined. The classification of the Haar-like features is done by the Relevance Vector Machine (RVM).