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This paper proposes a 3D facial mesh detection algorithm based on the geometric saliency of surface. Specifically, the geometric saliency of each vertex on 3D triangle mesh is measured by the combination of Gaussian-weighted curvature and spin-image correlation. Salient vertices with similar properties are clustered into regions on the saliency map, and represented as nodes by the graph model. To detect a 3D facial mesh, initialization and registration steps are applied to match each triangle in the graph model with a reference graph, corresponding to a 3D reference facial mesh. Furthermore, the match error between the graph model of the testing 3D mesh and the reference facial mesh is computed to classify face and non-face meshes. Experimental results demonstrate that the proposed algorithm is effective to detect 3D facial meshes and robust to facial expressions and geometric noises.