Three-dimensional human face reestablishment method and system based on edge graph

Abstract

The invention relates to a three-dimensional human face reestablishment method and system based on an edge graph. The method comprises the steps that first, according to human face feature points of a two-dimensional graph, a two-dimensional human face edge line is generated; second, according to a three-dimensional human face sample base obtained after standardization, a three-dimensional human face edge graph is established, and meanwhile the three-dimensional human face sample base obtained after standardization is subjected to main component decomposing to obtain feature vectors; third, through a genetic algorithm, the two-dimensional human face edge line of a target image and the three-dimensional human face edge graph are matched, optimization is carried out, and a three-dimensional reestablishing coefficient sequence of the target image is obtained; and fourth, the feature vectors in the second step and the three-dimensional reestablishing coefficient sequence in the third step are synthesized to reestablish a three-dimensional human face model. The problem of human face posture changing is solved, errors caused on reestablishing from feature point calibration are greatly reduced, and the problem that a sparse deforming model is sensitive to the feature points is well solved.

Description

technical field [0001] The invention relates to the fields of computer graphics, digital image processing, artificial intelligence, etc., in particular to a method and system for three-dimensional human face modeling through plane images. Background technique [0002] There are many classic methods for face reconstruction. For example, the method of deformation model reconstruction has been a research hotspot in recent years. It uses the idea of ​​linear combination to decompose existing samples into feature subspaces, and use the basis in the feature subspace to represent specific instances. . However, since the deformation model seeks the global optimal solution through an iterative method, the optimization process is extremely time-consuming through iterative optimization of the three-dimensional model. In addition, the sparse deformation model method restores the depth information of the face by approximating the deformation parameters based on a small number of facial ...

AI Technical Summary

Problems solved by technology

However, the sparse deformable model has two main shortcomings. One is that it has poor adaptability to face poses and cannot reconstruct points with pose changes. The other is that it requires high calibration accuracy of target feature points, and Point calibration directly affects the reconstruction accuracy of the algorithm

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