Tensor-based Active Appearance Model

Abstract

The active appearance model (AAM) is a well-known model that can represent a nonrigid object effectively. However, the fitting result is often unsatisfactory when the input image has pose, expression, and illumination variations. To overcome this problem, we propose a tensor-based AAM which consists of two kinds of tensors: image tensor and model tensor. The image tensor is used to estimate the image variation such as the pose, the expression, and the illumination by finding the basis subtensor with minimal reconstruction error. The model tensor is used to generate the specific AAM basis vectors by indexing the model tensor in terms of the estimated image variations. Experimental results show that the proposed tensor-based AAM reduces the fitting error of the conventional AAM by about four pixels.