光学技术, 2018, 44 (3): 333, 网络出版: 2018-06-09   

基于改进SURF算法的人脸点云配准

Face point cloud registration based on improved SURF algorithm
作者单位
1 南京理工大学 理学院, 南京 210094
2 北京仁光科技有限公司, 北京 100089
3 中国国防科技信息中心, 北京 100089
摘要
精准的三维人脸重建是三维人脸识别、三维人脸表情仿真等技术实现的重要前提。基于以往图像特征点和点云数据的三维配准算法研究, 提出了一种计算量小、实时性较高的人脸配准算法。提取人脸图像特征点, 计算64维的SURF描述符; 利用RANSAC算法剔除不稳定匹配点; 利用奇异值分解SVD求解粗配准变换矩阵; 利用改进的最近点迭代算法求解最终变换矩阵。实验结果显示配准误差只有8.71895×10-5m2, 总耗时为6.61s, 相比较SIFT算法和手动寻找匹配点, 速度快、精度高。
Abstract
The accurate 3D face reconstruction is an important prerequisite for 3D face recognition and 3D facial expression simulation. A face registration algorithm with small computation and high real-time is proposed based on previous research of image feature point and three-dimensional registration algorithm for point cloud data. The feature points of face images are extracted and 64 dimensional SURF descriptors are computed. Using the RANSAC algorithm, the unstable matching points are eliminated. The 2D image feature matching points are introduced into the 3D point cloud data, and the coarse registration transformation matrix is solved by singular value decomposition (SVD). The improved ICP algorithm is used to solve the final transform matrix. The experimental results show that the registration error is only 8.71895×10-5m2, and the total time consuming is 6.61 seconds. Compared with the SIFT algorithm and the manual search matching point, the algorithm proposed is not only fast but also accurate.

郭昱, 佘二永, 王清华, 李振华. 基于改进SURF算法的人脸点云配准[J]. 光学技术, 2018, 44(3): 333. GUO Yu, She Eryong, WANG Qinghua, LI Zhenhua. Face point cloud registration based on improved SURF algorithm[J]. Optical Technique, 2018, 44(3): 333.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!