论文标题
通过眼动预测性别
Predicting Gender via Eye Movements
论文作者
论文摘要
在本文中，我们报告了通过眼动的性别预测的第一个稳定结果。我们使用一个带有面部图像的数据集作为刺激和大量370名参与者。稳定性对我们有两种含义：首先，我们能够估算单个预测实验的标准偏差（SD）（约为4.1％）；这是通过改变参与者人数来实现的。其次，我们能够提供具有非常低标准误差的平均准确性（SEM）：我们的精度为65.2％，SEM为0.80％；这是通过许多随机选择预测的训练和测试集来实现的。我们的研究表明，两个特定的分类器达到了最佳精度：随机森林和逻辑回归。我们的结果重新确认了先前的发现，即女性对刺激的左眼更有偏见。
In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli.
