A comparison of shape constrained facial feature detectors

We consider the problem of robustly and accurately locating facial features. The relative positions of different feature points are represented using a statistical shape model. We construct an individual detector for each feature point, which is used to generate a feature response image. The quality of a given hypothesised shape can be evaluated quickly by combining values from each response image. We use global search to predict the approximate position of the face, then refine the hypothesis using non-linear optimisation. The result is an algorithm capable of robustly and accurately matching a face model to new images, which we refer to as Shape Optimised Search (SOS). We describe SOS in detail and compare the performance of the algorithm when three different classes of feature detectors are used. We demonstrate that the approach is capable of outperforming the well known Active Appearance Model method.