Active Appearance Models (AAMs) represent facial images with generative models for both shape and appearance of the face. Despite their success, they enjoy limited performance when used on faces that were not part of the training set. Moreover, training them with a lot of examples degrades their effectiveness. This limits their applicability for tracking multiple unseen faces in unconstrained scenarios. In (Wu et al., 2008) these issues are addressed by learning a discriminative face model which is fitted by minimizing a cost function that is concave. It turns out that this is equivalent to learning a ranking function. The framework shows a dramatic improvement over AAMs in terms of alignment robustness and speed, enabling the simultaneous real-time tracking of tens of faces. The approach describes a general methodology applicable to the many problems (e.g. discriminative object tracking) that can be solved by learning the cost function to be optimized, and which it has been shown it can be imposed to be either concave or convex.


  1. CVPR
    Face alignment using boosted ranking models Wu, H., Liu, X., and Doretto, G. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008. Oral abstract bibTeX pdf