Sparse Orthogonal Tucker Decomposition for 2D+3D Facial Expression Recognition

Multimodal facial expression recognition (FER) has been becoming a promising research hotspot in recent years. In this paper, we propose a novel sparse orthogonal Tucker decomposition for 2D+3D facial expression recognition (FER). Firstly, we construct a 4D tensor model to explore the efficient structure and complement information between 2D and 3D data. Secondly, based on orthogonal Tucker decomposition, the tensor optimization model, which impose sparse constraint on the involved core tensor and factor matrices, are utilized to find strong interactions between the core tensor and factor matrices for better classification prediction. Finally, a alternating direction method is employed to effectively solve the proposed optimization problem. Numerical experiments are implemented on the BU-3DFE database to indicate the effectiveness of the proposed approach.