Orthogonal Tensor Neighborhood Preserving Embedding for facial expression recognition

In this paper a generalized tensor subspace model is concluded from the existing tensor dimensionality reduction algorithms. With this model, we investigate the orthogonality of the bases of the high-order tensor subspace, and propose the Orthogonal Tensor Neighborhood Preserving Embedding (OTNPE) algorithm. We evaluate the algorithm by applying it to facial expression recognition, where both the 2nd-order gray-level raw pixels and the encoded 3rd-order tensor-formed Gabor features of facial expression images are utilized. The experiments show the excellent performance of our algorithm for the dimensionality reduction of the tensor-formed data especially when they lie on some smooth and compact manifold embedded in the high dimensional tensor space.