贝叶斯读者识别的半参数模型
A Semiparametric Model for Bayesian Reader Identification
摘要：
我们研究的问题，识别个人的基础上，他们的特征注视模式在阅读任意文本。这个问题的动机是一个不引人注目的生物特征设置，其中用户在访问文档期间被观察到，但没有执行需要用户的时间和注意力的特定挑战协议。现有的模型在阅读过程中的注视控制的个体差异是基于简单的聚合特征的眼动，或依赖于参数密度模型来描述，例如，扫视幅度或字固定持续时间。我们开发了灵活的半参数模型的眼动在阅读过程中，密度推断下的高斯过程之前集中在一个参数分布家庭，预计近似的真实分布。对251名个体的阅读数据进行的实证研究显示，与最先进的技术相比，该方法有显着的改进。
英文摘要：
We study the problem of identifying individuals based on their characteristic gaze patterns during reading of arbitrary text. The motivation for this problem is an unobtrusive biometric setting in which a user is observed during access to a document, but no specific challenge protocol requiring the user's time and attention is carried out. Existing models of individual differences in gaze control during reading are either based on simple aggregate features of eye movements, or rely on parametric density models to describe, for instance, saccade amplitudes or word fixation durations. We develop flexible semiparametric models of eye movements during reading in which densities are inferred under a Gaussian process prior centered at a parametric distribution family that is expected to approximate the true distribution well. An empirical study on reading data from 251 individuals shows significant improvements over the state of the art.
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