Multi-objective Evolutionary 3D Face Reconstruction Based on Improved Encoder-Decoder Network

Abstract Three-dimensional (3D) face application has attracted significant attention the field of multimedia and image processing. However, the end-to-end 3D face reconstruction method is still immature, and there are some problems, such as overfitting caused by too few training sets and unacceptable 3D face texture alignment performance. Therefore, we design a novel approach to construct a 3D face, named multi-objective evolutionary 3D face reconstruction based on improved encoder–decoder network (MoEDN). This study introduces a regularization algorithm named feature map distortion (Disout); whose purpose is to strengthen the network generalization ability. Based on this, we construct a multi-objective evolutionary 3D face reconstruction model, in which decision variables are distortion probability, distorted block size, distorted intensity, probability step, and learning rate; and objective functions are loss and structural similarity (SSIM). We use four multi-objective evolutionary algorithms (NSGA-II, AGEII, NSLS, and MOEA/D) to optimize the proposed model. Experimental results demonstrate that NSLS has the best performance. In addition, compared with position map regression network (PRNet), 2D-assisted self-supervised learning (2DASL) and other state-of-the-art, the proposed model achieves better loss values and NME values. Therefore, the proposed multi-objective evolutionary 3D face reconstruction model has outstanding 3D facial reconstruction performance in large poses and face expression.