3D face recognition using covariance based descriptors

• New covariance descriptor for 3D face recognition.
• We studied geodesic distances on the manifold of SDP matrices.
• We use geodesic distances as metrics for 3D face recognition.
• Our method outperforms other state of the art methods on two challenging datasets.

In this paper, we propose a new 3D face recognition method based on covariance descriptors. 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 covariance descriptors are symmetric positive definite matrices which can be viewed as an inner product on the tangent space of (Symd+) the manifold of Symmetric Positive Definite (SPD) matrices. In this article, we study geodesic distances on the Symd+ manifold and use them as metrics for 3D face matching and recognition. We evaluate the performance of the proposed method on the FRGCv2 and the GAVAB databases and demonstrate its superiority compared to other state of the art methods.