Efficient 3D reconstruction for face recognition

  • Authors:
  • Dalong Jiang;Yuxiao Hu;Shuicheng Yan;Lei Zhang;Hongjiang Zhang;Wen Gao

  • Affiliations:
  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduated School of Chinese Academy of Sciences, Beijing 100039, China;Microsoft Research Asia, Beijing 100080, China;School of Mathematical Sciences, Peking University, Beijing 100871, China;Microsoft Research Asia, Beijing 100080, China;Microsoft Research Asia, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduated School of Chinese Academy of Sciences, Beijing 100039, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

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Abstract

Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient two-dimensional (2D)-to-three-dimensional (3D) integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and (3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.