Low Rank Tucker Decomposition for 2D+3D Facial Expression Recognition

Abstract

In this paper, a novel low rank Tucker decomposition for 3D facial expression recognition (LRTDFER) approach is proposed, in which the core tensor is utilized to characterize the low-rank attribute among the samples combined with the factor matrices with the graph regularization embedding. At first, a model based on a 4D tensor is constructed from the facial expression data. By Tucker decomposing the constructed 4D tensor, a resulting core tensor and factor matrices in different tensor modes are utilized to characterize the low-rankness among samples. Because of the loss of information during modelling the 4D tensor, the missing data from partly observed facial expression data are recovered by embedding the tensor completion. Finally, the proposed model is handled and solved by adopting the alternating direction method of multipliers (ADMM). Meanwhile, the classification prediction of facial expressions are implemented by Multi-class-SVM. Numerical experiments are conducted on BU-3DFE database. The experiment results have been verified that our proposed approach is more competitive.