Author Information

Kendra Schmid
David Marx
Ashok Samal

Abstract

Shape analysis is useful for a wide variety of disciplines and has many applications. There are many different approaches to shape analysis, one of which focuses on the analysis of shapes that are represented by the coordinates of predefined landmarks on the object. This paper introduces Tridimensional Regression, a technique that can be used for mapping images and shapes that are represented by sets of three-dimensional landmark coordinates. The degree of similarity between shapes can be quantified using the tridimensional coefficient of determination (R2). An experiment was conducted to evaluate the effectiveness of this technique to correctly match the image of a face with another image of the same face. These results were compared to the R2 values obtained when only two dimensions are used, and show using three dimensions increases the ability to correctly discriminate between faces.

Keywords

bidimensional regression, nonlinear regression, face recognition, landmark data, three-dimensional shape analysis

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Apr 27th, 8:15 AM

TRIDIMENSIONAL REGRESSION

Shape analysis is useful for a wide variety of disciplines and has many applications. There are many different approaches to shape analysis, one of which focuses on the analysis of shapes that are represented by the coordinates of predefined landmarks on the object. This paper introduces Tridimensional Regression, a technique that can be used for mapping images and shapes that are represented by sets of three-dimensional landmark coordinates. The degree of similarity between shapes can be quantified using the tridimensional coefficient of determination (R2). An experiment was conducted to evaluate the effectiveness of this technique to correctly match the image of a face with another image of the same face. These results were compared to the R2 values obtained when only two dimensions are used, and show using three dimensions increases the ability to correctly discriminate between faces.