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Interactive Feature Visualization and Detection for 3D Face Classification

Author

Listed:
  • Jason McLaughlin

    (Indiana University-Purdue University, USA)

  • Shiaofen Fang

    (Indiana University-Purdue University, USA)

  • Sandra W. Jacobson

    (Wayne State University, USA, and University of Cape Town, South Africa)

  • H. Eugene Hoyme

    (Sanford School of Medicine, USA)

  • Luther Robinson

    (State University of New York, USA)

  • Tatiana Foroud

    (Indiana University, USA)

Abstract

A new visual approach to the surface shape analysis and classification of 3D facial images is presented. It allows the users to visually explore the natural patterns and geometric features of 3D facial scans to provide decision-making information for face classification which can be used for the diagnosis of diseases that exhibit facial characteristics. Using surface feature analysis under a digital geometry analysis framework, the method employs an interactive feature visualization technique that allows interactive definition, modification and exploration of facial features to provide the best discriminatory power for a given classification problem. OpenGL based surface shading and interactive lighting are employed to generate visual maps of discriminatory features to visually represent the salient differences between labeled classes. This technique will be applied to a medical diagnosis application for Fetal Alcohol Syndrome (FAS) which is known to exhibit certain facial patterns.

Suggested Citation

  • Jason McLaughlin & Shiaofen Fang & Sandra W. Jacobson & H. Eugene Hoyme & Luther Robinson & Tatiana Foroud, 2011. "Interactive Feature Visualization and Detection for 3D Face Classification," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 5(2), pages 1-16, April.
    • Handle: RePEc:igg:jcini0:v:5:y:2011:i:2:p:1-16
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