  Active Appearance Model (AAM) is a commonly used method for facial image
analysis with applications in face identification and facial expression
recognition. This paper proposes a new approach based on image alignment for
AAM fitting called bidirectional warping. Previous approaches warp either the
input image or the appearance template. We propose to warp both the input
image, using incremental update by an affine transformation, and the appearance
template, using an inverse compositional approach. Our experimental results on
Multi-PIE face database show that the bidirectional approach outperforms
state-of-the-art inverse compositional fitting approaches in extracting
landmark points of faces with shape and pose variations.
