Three dimensinal face reconstruction from single image is a classical problem

and has found its use in video game production and film production. The recent demands have upgraded to reconstructing high-resolution 3D face with high computational efficiency and accuracy. However

monocular 3D reconstruction is an ill-posed problem in nature. Meanwhile

high-resolution 3D reconstruction involves great computational cost. Aiming at these problems

we propose a Method to achieve accurate and efficient high-resolution 3D face reconstruction from a single image. The proposed Method is mainly based on feature adaptation and Laplace deformation. A morphable model is built using a group of 3D face samples for feature adaptation. In detail

we select a set of salient 3D features from each 3D sample

then a new face can be obtained through a linear combination of these 3D feature sets. The parameters of the morphable model are the linear combination coefficients. In feature adaptation

a scaled orthogonal projection is used to model the projection from 3D space to 2D plane

and we adjust the projection parameters and the model parameters

so that the 2D projection of the face generated by the morphable model is as close as possible to the 2D facial features extracted from a given image. After model adaptation

the personalized 3D features are reconstructed by linearly combining 3D feature sets with the computed coefficients. To achieve high-resolution 3D reconstruction

we use the reconstructed 3D features to deform a generic 3D face of high-resolution. The deformation is based on Laplacian Method which uses Laplacian coordinates to represent the local geometric characteristics at each 3D point of face model. According to this Method

the Laplacian coordinates of all the points should remain unchanged after model deformation

and also

a set of 3D features should be very close to the reconstructed 3D features after model deformation. Then high-resolution and personalized 3D face can be obtained by solving a least square problem. At last

the realistic 3D face is obtained through texture synthesis. Content of main experiments: Experiments aim to test the accuracy and efficiency of the proposed Method by comparing it to existed Methods. To this end

we project a group of 3D facial feature sets onto 2D plane

and reconstruct 3D features from these 2D projections. The accuracy of feature adaptation is evaluated by comparing the reconstructed features with the true features. The execution time is recorded to represent the efficiency of feature adaptation. Then

we use 3D features extracted from a set of 3D faces to deform a generic 3D face

and compare the deformation Results with the original faces to evaluate the effect of model deformation Methods. At last

texture mapped realistic 3D face are demonstrated. Compared with alternating least square Method

the proposed feature adaptation Method converges faster with lower reconstruction error because our model fitting process is guided by the necessary condition of the optimal solution. Compared with interpolation Method based on radial basis function

the Laplacian Method can deform face model in more reasonable way so that the reconstructed faces are closer to the original faces. This is because Laplacian Method maintains local geometric characteristics at every 3D point during face deformation

thus leads to better Result than interpolating using Euclidean distance metric. Also

the textured 3D faces have decent visual appearance owing to the high resolution of the face. This paper proposes to combine feature adaptation and Laplacian deformation for high-resolution 3D face reconstruction. Quantitative experiments indicate that our Method has better efficiency and accuracy compared with existed Methods. Also

qualitative experiments demonstrate impressive visual effect of the textured 3D face. Therefore

the proposed Method is very suitable for high-resolution 3D face reconstruction.