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Hierarchical covariance description for 3D face matching and recognition under expression variation

In this paper, we propose a hierarchical covariance description for 3D face matching and recognition under expression variation. Unlike feature-based vectors, covariance-based descriptors enable the fusion and the encoding of different types of features and modalities into a compact representation. The efficiency of covariance descriptors however may depend on the size of its region of definition. On the one hand, co-varying features in a small region do not capture sufficient properties of the face. On the other hand, large regions only capture coarse features, which may not be sufficiently discriminative. In this paper, we propose to represent a 3D face using a set of feature points. Around each feature point, we consider three covariance description levels. In our experiments, we demonstrate the utility of this representation and present challenging results on different datasets including the BU-3DFE and the GAVAB datasets.