Abstract
Traditional age estimation methods are prone to subjectivity, leading to a decrease in the reliability and repeatability of estimated ages in skeletal assemblages. In an attempt to reduce the level of subjectivity, this research applied a computational method designed to analyze surface topography, Dirichlet Normal Energy (DNE), to provide a mathematical assessment of age-related degeneration in the auricular surface. Reconstructed 3D models of 153 archaeological individuals were created by laser scanning and analyzed using the R studio package MolaR. DNE values showed moderate correlations with age phase (Buckberry-Chamberlain and Lovejoy), for the auricular surface as a whole as well as a number of topographical features (surface undulation, apical activity, macroporosity). Most encouragingly, this method had an extremely low levels of intra-observer error, which makes it repeatable and potentially more objective than traditional age estimation methods.
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