Item

Abstract

The sex of a face is perhaps its most salient feature. A principal components analysis (PCA) was applied separately
to the three-dimensional structure and texture data from laser-scanned human heads. Individual components from both analyses captured information related to the sex of the face. Notably, single projection coefficients characterized complex structural differences between three-dimensional male and female heads and between male and female texture maps. In a series of simulations, we compared the quality of the information available in the head versus texture data for predicting in the sex of the face. The results indicated that the three-dimensional structural data supported more accurate sex classification than the texture data, across a range of PCA-compressed
(dimensionality-reduced) representations of the heads. This kind of dual face representation can give insight into the nature of the information available to humans for categorizing and remembering faces.