3D Facial expression recognition using orthogonal tensor marginal fisher analysis on geometric maps

In this paper, a novel 3D facial expression recognition algorithm by Orthogonal Tensor Marginal Fisher Analysis (OTMFA) on geometric maps was proposed. In order to describe expressions properly in detail and simplify calculation, five kinds of geometric 2D maps including Depth Map (DM), three Normal Maps (NOMs) and Shape Index Map (SIM) are extracted. These maps are treated as second order tensors and they are used to learn low dimensional tensor subspaces by OTMFA, where preserves the manifold structure of expressions and reduces dimensions simultaneously. Next, features are further extracted by projecting these geometric maps into tensor subspaces. Finally, multi-class SVM classifiers are trained and tested respectively for each maps and their contributions are combined using the sum rule for final recognition. The effectiveness of proposed method is verified on BU-3DFE database, and the performance achieves an average of 88.32% and 84.27%, which is comparable to the state of the art ones.