Visual tracking is a key component in many computer vision applications. Linear subspace techniques (e.g. eigen-tracking) are one of the most popular approaches to align templates with appearance variations (e.g. illumination, iconic changes). A number of well known tracking algorithms have been proposed in the last years to accurately fit these models to images. Computational efficiency is an important limitation in object tracking algorithms and different efficient techniques, such as the “projected-out” optimization, have been proposed. They reduce the computational cost using an efficient formulation in which many of the involved operations can be precomputed. On the other hand, alternative “simultaneous” algorithms jointly optimize pose and appearance parameters, providing better performance but increasing the computational cost. In this paper, we propose an algorithm for efficient linear appearance model fitting based on the inverse compositional simultaneous optimization of pose and appearance. We introduce a novel formulation which reduces the required computational time while maintaining similar convergence properties of previous “simultaneous” approaches. Experimental results illustrate the capabilities of this algorithm in face tracking.

Appearance Models have been applied to model the space of human faces over the last two decades. In particular, Active Appearance Models (AAMs) have been successfully used for face tracking, synthesis and recognition, and they are one of the state-of-the-art approaches due to its efficiency and representational power. Although widely employed, AAMs suffer from a few drawbacks, such as the inability to isolate pose, identity and expression changes. This paper proposes Bilinear Active Appearance Models (BAAMs), an extension of AAMs, that effectively decouple changes due to pose and expression/identity. We derive a gradient-descent algorithm to efficiently fit BAAMs to new images. Experimental results show how BAAMs improve generalization and convergence with respect to the linear model. In addition, we illustrate decoupling benefits of BAAMs in face recognition across pose. We show how the pose normalization provided by BAAMs increase the recognition performance of commercial systems.