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The recognition of faces under varying expressions is one of the current challenges in the face recognition community. In this paper, we propose a method fusing different complementary approaches each dealing with expression variations. The first approach uses an isometric deformation model and is based on the largest singular values of the geodesic distance matrix as an expression-invariant shape descriptor. The second approach performs recognition on the more rigid parts of the face that are less affected by expression variations. Several fusion techniques are examined for combining the approaches. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 5.85% for the verification scenario and a rank 1 recognition rate of 94.48% for the identification scenario using the sum rule as fusion technique. This result outperforms other 3D expression-invariant face recognition methods on the same database.