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
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Schmid, Kendra; Marx, David; and Samal, Ashok
(2007).
"TRIDIMENSIONAL REGRESSION,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1105
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.
