Synthesis of multiple pose facial images using tensor-based subspace learning method

Facial pose synthesis has many useful applications in practice. How to synthesize facial pose images robustly and simply is still a challenging problem. In this paper we proposed a tensor-based subspace learning method (TSL) that makes possible the synthesis of human multi-pose facial images from a single 2D image. We organize 2D multi-pose images in a tensor form and apply tensor decomposition to build a projection subspace. An input 2D image is projected into the projection subspace to get a corresponding identity vector. The identity vector is used to generate the novel pose images. The experiments are performed on MaVIC (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) and preliminary experimental results show the effectiveness of our proposed method.