Evolution Strategies for Matching Active Appearance Models to Human Faces

Face registration is a challenging problem due in part to the non-rigid nature of human faces. Active Appearance Models (AAMs) have been proposed as a useful technique for face registration in part because they can account for changes in shape. Fitting of AAMs to imagery is typically done using the Gauss-Newton method; however this approach is known to fail when either the initial shape estimate is to far off and/or the appearance model fails to direct search toward a good match. In this paper, we employ Evolution Strategies (ES) to search for a near optimal fit, i.e. set of model parameters, that relate an AAM to a novel face image. In addition, we dramatically reduce the dimensionality of the search problem by analytically determining the optimal texture parameters associated with any given shape. Experimental results reveal that the proposed approach outperforms the standard AAM parameter estimation technique and some of its variants. Further, the difference is statistically significant. Finally, some basic limitations of AAMs are identified using fitness landscape analysis.