Background modeling for generative image models

Face image interpretation with generative models is done by reconstructing the input image as well as possible. A comparison between the target and the model-generated image is complicated by the fact that faces are surrounded by background. The standard likelihood formulation only compares within the modeled face region. Through this restriction an unwanted but unavoidable background model appears in the likelihood. This implicitly present model is inappropriate for most backgrounds and leads to artifacts in the reconstruction, ranging from pose misalignment to shrinking of the face. We discuss the problem in detail for a probabilistic 3D Morphable Model and propose to use explicit image-based background models as a simple but fundamental solution. We also discuss common practical strategies which deal with the problem but suffer from a limited applicability which inhibits the fully automatic adaption of such models. We integrate the explicit background model through a likelihood ratio correction of the face model and thereby remove the need to evaluate the complete image. The background models are generic and do not need to model background specifics. The corrected 3D Morphable Model directly leads to more accurate pose estimation and image interpretations at large yaw angles with strong self-occlusion. (C) 2015 Elsevier Inc. All rights reserved.