Three-dimensional face super-resolution method based on multi-frame point cloud fusion and variable model

Abstract

The invention discloses a three-dimensional face super-resolution method based on multi-frame point cloud fusion and a variable model. The method comprises the following steps: S1, acquiring a video frame depth image and a point cloud sequence Pi belonging to P; s2, calculating a rough fitting result of the variable model by taking the average face as a template and Pi as a target point cloud, andobtaining a first rough fitting score; s3, screening the Pi according to the first rough fitting score to obtain a successfully detected point cloud set Pf; s4, taking a first point cloud P0 in the Pf as a target point cloud, taking all the rest face point clouds Pr in the Pf as templates, and respectively registering the P0 to obtain a second rough fitting score; s5, screening Pr according to the second rough fitting score, converting the screened point cloud to the position where P0 is located, and Palign = {P0, Pj0} is obtained; s6, converting the Palign to obtain a smooth fusion point cloud Pfusion; s7, performing variable fitting on the object Pk in the Palign by using a three-dimensional face variable model, and generating a variable model face fusion point cloud Mavg; and S8, fusing the Pfuse and the Mavg to obtain a three-dimensional human face super-resolution point cloud Poutput. According to the invention, the high-precision face point cloud can be obtained.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a three-dimensional face super-resolution method based on multi-frame point cloud fusion and a variable model. Background technique [0002] Face recognition is a biometric technology for identification based on human facial feature information. A series of related technologies that use a video camera or camera to collect images or video streams containing human faces, automatically detect and track human faces in the images, and then perform facial recognition on the detected faces, usually also called portrait recognition and facial recognition. . Point cloud super-resolution or point cloud fusion are two commonly used methods in face recognition technology, mainly to better reconstruct the face surface. [0003] Most of the existing point cloud super-resolution or point cloud fusion methods, by mapping the 3D point cloud information to the 2D image space and then pe...

AI Technical Summary

Problems solved by technology

[0003] Most of the existing point cloud super-resolution or point cloud fusion methods, by mapping the 3D point cloud information to the 2D image space and then performing data fusion, have the following disadvantages: 1. The mapping conversion from 3D to 2D will result in a large amount of information Lost, thereby reducing the accuracy of the fused data

2. For situations such as large gestures and expressions of faces, two-dimensional images cannot represent these details well, which may easily lead to fusion failure

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