28 Blinks Later
28 Blinks Later
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28 眨眼之后
DOI：
10.1145/3319535.3354233
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Eberz S
中科院分区：
文献类型：
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作者：
Eberz S
在这项工作中，我们解决了三个被忽视的基于眼动生物识别的连续认证系统的实际挑战：（i）照明条件的变化，（ii）任务相关的功能和（iii）需要一个准确的校准阶段。我们收集了22名参与者的眼动数据。为了测量这三个挑战的效果，我们在改变实验条件的同时收集数据：用户执行四个不同的任务，照明条件在会话过程中发生变化，我们收集与准确（用户特定）和不准确（通用）校准相关的数据。为了应对不断变化的照明条件，我们确定了两个主要的光源，即，屏幕亮度和环境光，我们提出了一个瞳孔直径校正机制的基础上。我们发现，这种机制可以准确地调整瞳孔收缩或扩大有关的变化量的光到达眼睛。为了解释不准确的校准，我们基于双目跟踪（其中左眼和右眼被单独跟踪）用新特征来增强先前已知的特征集。我们表明，这些功能可以是非常独特的，即使使用通用校准。我们进一步应用基于人口数据的跨任务映射函数，该函数系统地考虑了特征对任务的依赖性（例如，阅读文本和浏览网站导致不同的眼球运动动态）。使用这些增强功能，即使放松对实验条件的假设，我们表明，我们的系统实现了显着降低错误率相比，以前的工作。对于任务内身份验证，在没有用户特定校准以及可变屏幕亮度和环境照明的情况下，我们仅用两分钟的训练数据就实现了3.93%的同等错误率。对于相同的设置但具有恒定的屏幕亮度（例如，对于阅读任务），我们可以实现低至1.88%的相等错误率。
In this work we address three overlooked practical challenges of continuous authentication systems based on eye movement biometrics: (i) changes in lighting conditions, (ii) task dependent features and the (iii) need for an accurate calibration phase. We collect eye movement data from 22 participants. To measure the effect of the three challenges, we collect data while varying the experimental conditions: users perform four different tasks, lighting conditions change over the course of the session and we collect data related to both accurate (user-specific) and inaccurate (generic) calibrations. To address changing lighting conditions, we identify the two main sources of light, i.e., screen brightness and ambient light, and we propose a pupil diameter correction mechanism based on these. We find that such mechanism can accurately adjust for the pupil shrinking or expanding in relation to the varying amount of light reaching the eye. To account for inaccurate calibrations, we augment the previously known feature set with new features based on binocular tracking, where the left and the right eye are tracked separately. We show that these features can be extremely distinctive even when using a generic calibration. We further apply a cross-task mapping function based on population data which systematically accounts for the dependency of features to tasks (e.g., reading a text and browsing a website lead to different eye movement dynamics). Using these enhancements, even while relaxing assumptions about the experimental conditions, we show that our system achieves significantly lower error rates compared to previous work. For intra-task authentication, without user-specific calibration and in variable screen brightness and ambient lighting, we achieve an equal error rate of 3.93% with only two minutes of training data. For the same setup but with constant screen brightness (e.g., as for a reading task) we can achieve equal error rates as low as of 1.88%.