Automated Face Identification Using Volume-Based Facial Models

  • Jeffrey Huang
  • Neha Maheshwari
  • Shiaofen Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


Face represents complex, multi dimensional, meaningful visual stimuli. Computational models for face recognition represent the problem as a high dimensional pattern recognition problem. This paper introduces an innovative facial identification method using eigenface approach on volume-based graphics rather than 2D photo-images. We propose to convert polygon mesh surface to a volumetric representation by regular sampling in a volumetric space. Our motivation is to extend existing 2D facial analysis techniques to a 3D image space by taking advantage of use of the volumetric representation. We apply principle component analysis (PCA) for dimensionality reduction. Face feature patterns are projected onto a lower dimensional PCA sub-space that spans the known facial patterns. 3D eigenface feature space is constructed for face identification.


Face Recognition Face Image Principle Component Analysis Cosine Distance Symmetry Curve 
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

  • Jeffrey Huang
    • 1
  • Neha Maheshwari
    • 1
  • Shiaofen Fang
    • 1
  1. 1.Department of Computer and Information ScienceIndiana University Purdue UniversityIndianapolisUSA

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