Abdi H, Valentin D, Edelman B, O'Toole A J
School of Human Development, University of Texas at Dallas, Richardson 75083-0688, USA.
Perception. 1995;24(5):539-62. doi: 10.1068/p240539.
The ability of a statistical/neural network to classify faces by sex by means of a pixel-based representation has not been fully investigated. Simulations with pixel-based codes have provided sex-classification results that are less impressive than those reported for measurement-based codes. In no case, however, have the reported pixel-based simulations been optimized for the task of classifying faces by sex. A series of simulations is described in which four network models were applied to the same pixel-based face code. These simulations involved either a radial basis function network or a perceptron as a classifier, preceded or not by a preprocessing step of eigendecomposition. It is shown that performance comparable to that of the measurement-based models can be achieved with pixel-based input (90%) when the data are preprocessed. The effect of the eigendecomposition preprocessing of the faces is then compared with spatial-frequency analysis of face images and analyzed in terms of the perceptual information it captures. It is shown that such an examination may offer insight into the facial aspects important to the sex-classification process. Finally, the contribution of hair information to the performance of the model is evaluated. It is shown that, although the hair contributes to the sex-classification process, it is not the only important contributor.
统计/神经网络通过基于像素的表示方式按性别对面部进行分类的能力尚未得到充分研究。基于像素编码的模拟所提供的性别分类结果，不如基于测量编码所报告的结果那么令人印象深刻。然而，在任何情况下，所报告的基于像素的模拟都未针对按性别对面部进行分类的任务进行优化。本文描述了一系列模拟，其中将四种网络模型应用于相同的基于像素的面部编码。这些模拟涉及使用径向基函数网络或感知器作为分类器，并在其之前进行或不进行特征分解的预处理步骤。结果表明，当数据经过预处理时，基于像素的输入（90%）可以实现与基于测量的模型相当的性能。然后将面部的特征分解预处理效果与面部图像的空间频率分析进行比较，并根据其捕获的感知信息进行分析。结果表明，这样的检查可能有助于深入了解对性别分类过程重要的面部特征。最后，评估了头发信息对模型性能的贡献。结果表明，虽然头发对性别分类过程有贡献，但它不是唯一的重要因素。
