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Abstract

The sex of a face is perhaps its most salient feature. A principal components analysis (PCA) was applied separately to the three-dimensional
(3-D) structure and graylevel image (GLI) 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 differences between the 3-D structure of male and female
heads and between male and female GLI maps. In a series of simulations, the quality of the information available in the 3-D head versus GLI data for
predicting the sex of the face has been compared. The results indicated that the 3-D head data supported more accurate sex classification than the GLI
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.