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.
ASJC Scopus subject areas
- Experimental and Cognitive Psychology
- Sensory Systems
- Artificial Intelligence