O'Toole A J, Vetter T, Troje N F, Bülthoff H H
School of Human Development, University of Texas at Dallas, Richardson 75083-0688, USA.
Perception. 1997;26(1):75-84. doi: 10.1068/p260075.
The sex of a face is perhaps its most salient features. 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.
面部的性别特征或许是其最为显著的特点。我们分别对激光扫描的人类头部的三维（3-D）结构和灰度图像（GLI）数据进行了主成分分析（PCA）。这两种分析中的各个成分都捕捉到了与面部性别相关的信息。值得注意的是，单个投影系数刻画了男性和女性头部三维结构之间以及男性和女性GLI图谱之间的复杂差异。在一系列模拟中，我们比较了3-D头部数据与GLI数据中用于预测面部性别的可用信息质量。结果表明，在头部的一系列PCA压缩（降维）表示中，3-D头部数据比GLI数据支持更准确的性别分类。这种对面部的双重表示能够深入了解人类用于对面部进行分类和记忆的可用信息的本质。
