Interactive Feature Visualization and Detection for 3D Face Classification

Jason McLaughlin, Shiaofen Fang, Sandra W. Jacobson, H. Eugene Hoyme, Luther Robinson and Tatiana Foroud
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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

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2011, vol. 5, issue 2, 1-16

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.

Date: 2011
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