Sparse Multi-Feature Tensor Representation for 3D Facial Expression Recognition

In this paper, we propose a sparse multi-feature tensor-based representation (SMFTR) for 3D facial expression recognition. Overcome the defects of vector-based and matrix-based models, our method develops a tensor model which mix 2D and 3D information and then obtained the sparse tensor feature under Tucker decomposition framework. In details, a high-order tensor model is constructed by multi-feature images that extracted from 2D and 3D facial expression data. The tensor model joints various useful infromation from multiple aspects, including geometric properties, texture information and surface deformation, etc. Then, in order to reduce the redundancy and extract discriminative feature, 3D method of optimal directions (MOD) dictionary learning algorithm is used to learn sparse dictionaries and N-way block orthogonal matching pursuit (OMP) is used to calculate the sparse coefficient tensor which is used as the tensor feature for classification. Finally, the performance of our method is demonstrated on BU-3DFE database by the non-linear support vector machine (SVM) classifier. The experimental results illustrated that the proposed method achieves a competitive accurate rate for 3D facial expression recognition.