Multi-pose 3D facial landmark localization based on multi-model information

This paper proposes a novel multi-pose 3D facial landmark localization method based on multi-model information. In the presented method, affine invariant Affine-SIFT was utilized to 2D face texture image for feature point detection, the detected points were then mapped into the corresponding 3D face model. For 3D facial surface, the local neighbor curvature maximum change and iterative constraint optimization were combined to complete the facial landmark localization. The proposed method does not need estimate and define the posture of the face model and the format of 3D face mesh, therefore is more suitable for practical application. Experimental results on FRGC2.0 and NPU3D face database show the proposed method is robust to face pose change, and has higher localization accuracy compared with the existing methods.