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Active Appearance Models (AAMs) represent the shape and appearance of an object via two low-dimensional subspaces, one for shape and one for appearance. AAMs for facial images are currently receiving considerable attention from the vision community. However, most existing work focuses on fitting AAMs to high-quality facial images. For many applications, effectively fitting an AAM to low-resolution facial images is of critical importance. This paper addresses this challenge from two aspects. On the modeling side, we propose an iterative AAM enhancement scheme, which not only results in increased fitting speed, but also improves the fitting robustness. For fitting AAMs to low-resolution images, we build a multi-resolution AAM and show that the best fitting performance is obtained when the model resolution is slightly higher than the facial image resolution. Experimental results using both indoor video and outdoor surveillance video are presented. 1
Liu et al. (Sun,) studied this question.