3D Face Model Estimation based on Similarity Transform using Differential Evolution Optimization

3D Face model reconstruction from a single 2D image is fundamentally important for face recognition because the 3D model is invariant to changes of viewpoint, illumination, background clutter and occlusions. In this paper, an efficient 3D Face Reconstruction algorithm is proposed based on multi-view 2D images of human face based on similarity transform measurements. In this algorithm, the pose and depth estimation from 2D feature points of the respective face images is considered as an optimization problem and solved using Differential Evolution optimization. Further different strategies of differential evolution are used to optimize and the results are compared. Simple model integration method is proposed to improve the estimation accuracy of the 3D structure of face, when more than one non-frontal-view face images are available. Furthermore, Pearson Linear Correlation is computed to show the efficiency of the proposed approach. Experimental results on 2D Head Pose and 3D Bosphorus databases demonstrate the feasibility and efficiency of the proposed methods.