Segmented AAMs Improve Person-Independent Face Fitting

An Active Appearance Model (AAM) is a variable shape and appearance model built from annotated training images. It has been largely used to synthesize or fit face images. Person-independent face AAM fitting is a challenging open issue. For standard AAMs, fitting a face image for an individual which is not in the training set is often limited in accuracy, thereby restricting the range of application. As a first contribution, we show that the limitation mainly comes from the inability of the AAM appearance counterpart to generalize, i.e. to accurately generate previously unseen visual data. As a second contribution, we propose an efficient person-independent face fitting framework based on what we call multi-level segmented AAMs. Each segment encodes a physically meaningful part of the face, such as an eye. A coarse-to-fine fitting strategy with a gradually increasing number of segments is used in order to ensure a large convergence basin. Fitting accuracy is assessed by comparison with manual labelling statistics constructed from multiple data annotations. Experimental results support the claim that standard AAMs are well-adapted to person-specific fitting while segmented AAMs outperform the classical AAMs in a personindependent context in terms of accuracy, and ability to generate new faces.