Automatic Pose-Normalized 3D Face Modeling and Recognition Systems

  • Sunjin Yu
  • Kwontaeg Choi
  • Sangyoun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Pose-variation factors present a big problem in 2D face recognition. To solve this problem, we designed a 3D face acquisition system which was able to generate multi-view images. However, this created another pose-estimation problem in terms of normalizing the 3D face data. This paper presents an automatic pose-normalized 3D face data acquisition method that is able to perform both 3D face modeling and 3D face pose-normalization at once. The proposed method uses 2D information with the AAM (Active Appearance Model) and 3D information with a 3D normal vector. The proposed system is based on stereo vision and a structured light system which consists of 2 cameras and 1 projector. In orsder to verify the performance of the proposed method, we designed an experiment for 2.5D face recognition. Experimental results showed that the proposed method is robust against pose variation.


Face Recognition Stereo Vision Active Appearance Model Epipolar Geometry Face Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sunjin Yu
    • 1
  • Kwontaeg Choi
    • 2
  • Sangyoun Lee
    • 1
  1. 1.Dept. of Electrical and Electronics EngineeringBiometrics Engineering Research CenterSeoulSouth Korea
  2. 2.Dept. of Computer ScienceYonsei University, Biometrics Engineering Research CenterSeoulSouth Korea

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