Facial Contour Labeling via Congealing

It is a challenging vision problem to discover non-rigid shape deformation for an image ensemble belonging to a single object class, in an automatic or semi-supervised fashion. The conventional semi-supervised approach [1] uses a congealing-like process to propagate manual landmark labels from a few images to a large ensemble. Although effective on an inter-person database with a large population, there is potential for increased labeling accuracy. With the goal of providing highly accurate labels, in this paper we present a parametric curve representation for each of the seven major facial contours. The appearance information along the curve, named curve descriptor, is extracted and used for congealing. Furthermore, we demonstrate that advanced features such as Histogram of Oriented Gradient (HOG) can be utilized in the proposed congealing framework, which operates in a dual-curve congealing manner for the case of a closed contour. With extensive experiments on a 300-image ensemble that exhibits moderate variation in facial pose and shape, we show that substantial progress has been achieved in the labeling accuracy compared to the previous state-of-the-art approach.