电子器件2025，Vol.48Issue(1)：116-122,7.DOI:10.3969/j.issn.1005-9490.2025.01.019
基于优化形变统计模型的3D人脸识别
3D Face Recognition Based on Optimal Deformation Statistical Model
蔡梦园 1袁三男1
作者信息
- 1. 上海电力大学电子与信息工程学院,上海 201306
- 折叠
摘要
Abstract
In order to solve the problem of low accuracy of face recognition caused by the lack of large-scale 3D face datasets,a 3D face recognition method based on Gaussian statistical shape models(GPSM)is proposed.First,in the reference data set,GPSM is applied to obtain the 3D face shape GPSM model,the 3D face expression GPSM model and the 3D face texture GPSM model,and the three GPSM models are linearly combined to generate a large number of 3D faces,forming a large 3D face dataset.Next,the face synthesized by GPSM is preprocessed.Then,the preprocessed face is used to train the 3D face recognition network of Res-GLNet.Finally,the perform-ance of Res-GLNet is tested on the FRGCv2 and Bosphorus public datasets.The results show that the proposed method achieves 98.9%and 98.67%recognition rates respectively,which are better than Pointnet,Pointnet++,Pointcnn and other recognition methods,proving that the proposed method can greatly improve the recognition accuracy.关键词
机器视觉/人脸合成/3D人脸识别/人脸点云Key words
machine vision/face synthesis/3D face recognition/face point cloud分类
信息技术与安全科学引用本文复制引用
蔡梦园,袁三男..基于优化形变统计模型的3D人脸识别[J].电子器件,2025,48(1):116-122,7.
