Fast nonnegative tensor factorization based on graph-preserving for 3D facial expression recognition

In this paper, we utilize 4D tensor structure to explore efficient structure information for 3D facial expression recognition. As a powerful tool to analyze multidimensional nonnegative tensor data, nonnegative tensor factorization (NTF) aims to obtain a partly localized representation of high-dimensional tensors. However, the NTF algorithms often suffer from both high computational complexity and memory requirements for high-order tensor data. To overcome these disadvantages, we present a low-rank approximation method to decrease the computation complexity. Meanwhile, we extract local geometric and discriminant information to improve facial recognition. Therefore, we propose fast nonnegative tensor factorization based on graph-preserving (FNTFGP) algorithm for 3D facial expression recognition (FER). It is a trial to explore and apply the proposed method into 3D FER. Experiments are conducted on the BU-3DFE database. The result of experiments shows the validity and efficiency of the proposed approaches. The proposed algorithm opens a promising direction for the higher performance of 3D facial expression recognition.