Low-compute facial expression recognition using fiducial feature-sets

Abstract

Facial Expression Recognition is an exciting area of affective computing. As mobile and embedded devices become increasingly ubiquitous, exploration of low-compute approaches for facial expression recognition is essential. Facial landmark points are fiducial features that are used to localize and represent salient regions of the face, such as eyes, nose and lips. Any facial expression can be expressed as an activation of facial muscles in specific parts of the face, thereby affecting the locations of the facial landmark points in those parts. This relationship can be captured concretely by deriving appropriate feature-sets from these points. In this paper, an approach for deriving three types of feature-sets from a set of facial landmark points detected on a face is discussed. Such feature-sets are derived for three standard facial expression recognition datasets with labelled expression classes. The derived features-sets are used as inputs for training computationally light machine-learning classifiers, yielding encouraging classification accuracy. These results are presented and discussed with both quantitative and qualitative observations.