Multi-view face recognition by nonlinear tensor decomposition

We discuss a new multi-view face recognition method that extends a recently proposed nonlinear tensor decomposition technique. We use this technique to provide a generative face model that can deal with both the linearity and nonlinearity in multi-view face images. Particularly, we study the effectiveness of three kinds of view manifold for multi-view face representation, i.e., the concept-driven, data-driven and hybrid data-concept-driven view manifolds. An EM-like algorithm is developed to estimate the identity and view factors iteratively. The new face generative model can successfully recognize face images captured under unseen views, and the experimental results provide the new method is superior to the traditional TensorFace-based algorithm and the view-based PCA method.