Automatic face annotation by multilinear AAM with Missing Values

It has been shown that multilinear subspace analysis is a powerful tool to overcome difficulties posed by viewpoint, illumination and expression variations in Active Appearance Model(AAM). However, the Higher Order Singular Value Decomposition (HOSVD) in multilinear analysis requires training samples to build the training tensor, which include face images under all different variations. It is hard to obtain such a complete training tensor in practical applications. In this paper, we propose a multilinear AAM which can be generated from an incomplete training tensor using Multilinear Subspace Analysis with Missing Values (M²SA). Also, the 2D appearance is used for training appearance tensor directly to reduce the memory requirements. Experimental results on the Multi-PIE face database show the efficiency of the proposed method.