Multi-view face identification and pose estimation using B-spline interpolation

  • Authors:
  • Frank Y. Shih;Camel Y. Fu;Kai Zhang

  • Affiliations:
  • Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal
  • Year:
  • 2005

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Abstract

The available face views in the training set are mostly limited. In this paper, we present a view interpolation method using nonlinear B-spline on face manifolds. Two models, the inner-outer ellipse model and the moment of inertia model, are developed to estimate the pose orientation. We use the limited view-pose face images to form the pose eigen space. Then, based on these nonlinear manifolds we form a B-spline for each individual. Identification is to compute the shortest Euclidean distance from a given test view to the nearest point within one of these B-splines. Once the test view is classified as a familiar individual in the training set, not only can the individual be identified, but also the pose angle can be estimated. Experimental results show that B-spline interpolation can achieve a recognition rate of 95%.