Efficient Graph-based Kernel using Covariance Descriptors for 3D Facial Expression Classification

In this paper, the problem of person-independent facial expression recognition (FER) is addressed using the 3D geometry information extracted from the 3D shape of the face. The 3D data can provide more information and more robust features under some conditions than the 2D data. State-of-the-art 3D FER methods are often based on a single descriptor which may fail to handle the large inter-class and intra-class variability of the human facial expressions. For this reason, we propose to explore the usage of covariance matrices of features, instead of directly using the local features for the 3D FER task. Since covariance matrices are elements of the non-linear manifold of Symmetric Positive Definite (SPD) matrices, we propose, in this paper, a manifold-based classification using a Graph-Matching Kernel method. The proposed Kernel is appropriate for SVM-based image classification, and thus is suitable for FER application. We evaluate the performance of the proposed method on the BU-3DFE and the Bosphorus datasets, and demonstrate its superiority compared to the state-of-the-art methods.