3D Model-Based Face Recognition in Video

  • Unsang Park
  • Anil K. Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Face recognition in video has gained wide attention due to its role in designing surveillance systems. One of the main advantages of video over still frames is that evidence accumulation over multiple frames can provide better face recognition performance. However, surveillance videos are generally of low resolution containing faces mostly in non-frontal poses. Consequently, face recognition in video poses serious challenges to state-of-the-art face recognition systems. Use of 3D face models has been suggested as a way to compensate for low resolution, poor contrast and non-frontal pose. We propose to overcome the pose problem by automatically (i) reconstructing a 3D face model from multiple non-frontal frames in a video, (ii) generating a frontal view from the derived 3D model, and (iii) using a commercial 2D face recognition engine to recognize the synthesized frontal view. A factorization-based structure from motion algorithm is used for 3D face reconstruction. The proposed scheme has been tested on CMU’s Face In Action (FIA) video database with 221 subjects. Experimental results show a 40% improvement in matching performance as a result of using the 3D models.

Keywords

Face recognition video surveillance 3D face modeling view synthesis structure from motion factorization active appearance model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Unsang Park
    • 1
  • Anil K. Jain
    • 1
  1. 1.Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, East Lasing, MI 48824USA

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