计算机应用 ›› 2012, Vol. 32 ›› Issue (11): 3189-3192.DOI: 10.3724/SP.J.1087.2012.03189

• 图形图像处理 • 上一篇    下一篇

基于特征分块的三维人脸重建和识别

鹿乐,周大可,胡阳明   

  1. 南京航空航天大学 自动化学院,南京 210016
  • 收稿日期:2012-05-22 修回日期:2012-07-02 发布日期:2012-11-12 出版日期:2012-11-01
  • 通讯作者: 鹿乐
  • 作者简介:鹿乐 (1987-),女,山东莱芜人,硕士研究生,主要研究方向: 三维人脸重建和识别; 周大可 (1974-), 男, 江苏涟水人, 副教授, 博士, 主要研究方向: 图像处理、计算机视觉; 胡阳明 (1988-),男,江苏盐城人, 硕士研究生, 主要研究方向: 三维人脸重建和识别。
  • 基金资助:
    国家自然科学基金资助项目(61172135);南京航空航天大学基本科研业务费专项科研项目(NS2010089)

3D face reconstruction and recognition based on feature division

LU Le1,ZHOU Da-ke1,HU Yang-ming2   

  1. 1. College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China
    2. College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China
  • Received:2012-05-22 Revised:2012-07-02 Online:2012-11-12 Published:2012-11-01
  • Contact: LU Le

摘要: 针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷,提出一种基于特征分块的三维人脸重建算法,并将此算法应用到三维人脸识别中,实现了基于特征分块的加权三维人脸识别。首先,利用基于平面模板的非均匀重采样法对原始数据进行归一化;其次,采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块;然后,利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建;最后,实现了此算法在三维人脸识别中的应用。实验表明,此重建算法具有较高的精度和重建效率,还可以达到全局最优,并且可以提高三维人脸的识别率。

关键词: 三维人脸重建, 特征分块, 主元分析, 形变模型, 三维人脸识别

Abstract: The traditional algorithm of 3D face reconstruction is inefficient and it is difficult to meet the requirements of practical application. To address this problem, a feature-slice-based 3D face reconstruction algorithm was proposed. Besides, the feature-slice-based weighed 3D face recognition was proposed on the basis of the reconstruction algorithm. First, a 2D template-based alignment algorithm was developed to process the correspondence between faces automatically, and a linear facial model was built up. Second, an improved Active Shape Model (ASM) algorithm was proposed to locate the feature points and slices in the 3D and 2D face images. Then, every facial feature slices shape was reconstructed by a PCA-based sparse morphable mode. Finally, the algorithm was applied to 3D face recognition. The experimental results show that the presented algorithm has higher efficiency and accuracy, and improves the 3D face recognition rate.

Key words: 3D face reconstruction, feature division, Principal Component Analysis (PCA), morphable mode, 3D face recognition

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