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

Frank Shih, Camel Fu, Kai Zhang

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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%.

Original languageEnglish (US)
Pages (from-to)189-204
Number of pages16
JournalInformation sciences
Volume169
Issue number3-4
DOIs
StatePublished - Feb 1 2005

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Keywords

  • Face recognition
  • Pattern recognition
  • Pose estimation
  • Principal component analysis
  • Spline interpolation

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