论文标题
使用眼睛的生成模型的判别查看器识别
Discriminative Viewer Identification using Generative Models of Eye Gaze
论文作者
论文摘要
我们研究了根据他们的眼睛目光识别观众的问题。心理学研究得出了眼动的生成随机模型。为了在歧视训练的分类模型中利用这些背景知识，我们从不同的眼睛凝视模型中得出了Fisher内核。在实验上，我们发现分类器的性能在很大程度上取决于潜在的生成模型。使用Fisher内核使用SVM改善了基本生成模型的分类性能。
We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model.
