3D morphable model (3DMM) has been widely used in 3D modeling,image synthesis and related fields.However,it is perplexed by over-constraint due to the influence from size,types,and principal components of training data,thus cannot provide enough flexibility to represent high-frequency deformation.Here,we embed the 3DMM into deep neural network to improve its representation ability in 3D face reconstruction.A dual-path neural network is constructed and improved in efficiency of network learning,which achieves balance between global path and local path.Then the nonlinear 3DMM is improved in both learning objectives and network structure,so as to capture more details than linear or previous nonlinear models.The comparison and simulation experiments show that the proposed algorithm has lower normalized average error in 3D face reconstruction,and the generated 3D face model has good robustness and accurate details.