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

Abstract

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.

Application of evolutionary and swarm optimization in computer vision: a literature survey

“…A facial surface is represented as a deformation of a generic 3D mesh, and its facial expression is classified based on a certain rule. Chandar and Savithri [92] introduced an algorithm for estimating a 3D face model from a face with a nonfrontal view. This algorithm regards an estimation as an optimization problem when searching for the pose parameters for a face a non-frontal view consisting of angles around the x-, y-, and z-axes, and for depth values of the facial feature points of a frontal-view 3D face model.…”

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

“…The article [38] summarizes different applications of evolutionary algorithms in the pattern recognition and machine learning including the Differential Evolution. The DE has been utilized for human body pose estimation from the point clouds [6, 36, 40], circles detection [7], ellipses detection [41], recognition of leukocytes in images, or 3D face model reconstruction utilizing multiview 2D images [42]. Most of the referenced algorithms optimize analytically a temporary pattern shape, deformable or active shape models.…”