Three-dimensional human head and face model reconstruction method based on random face image

Abstract

The invention provides a three-dimensional human head and face model reconstruction method based on a random face image. The method includes; establishing a human face bilinear model and an optimization algorithm by using a three-dimensional human face database; gradually separating the spatial attitude of the human face, camera parameters and identity features and expression features for determining the geometrical shape of the human face through the two-dimensional feature points, and adjusting the generated three-dimensional human face model through Laplace deformation correction to obtaina low-resolution three-dimensional human face model; finally, calculating the face depth, and achieving high-precision three-dimensional model reconstruction of the target face through registration ofthe high-resolution template model and the point cloud model, so as to enable the reconstructed face model to conform to the shape of the target face. According to the method, while face distortion details are eliminated, original main details of the face are kept, the reconstruction effect is more accurate, especially in face detail reconstruction, face detail distortion and expression influences are effectively reduced, and the display effect of the generated face model is more real.

Description

technical field

[0001] The invention relates to the technical fields of image processing and computer graphics, in particular to a method for reconstructing a three-dimensional human head and face model. Background technique

[0002] The human face is the most characteristic part of human beings. It not only contains obvious common features, but also reflects the different characteristics of each person. With the rapid development of computer graphics and image processing technology, the appearance description of human face has changed from two-dimensional to three-dimensional. Under the coverage of modern network big data, face images have become the data form with the lowest cost of face information, and it has become very easy to obtain personal face images. How to perform accurate 3D face recognition through a series of random image collections of target faces There is a lot of potential for refactoring. At the same time, the face information feature set contained in t...

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