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Eye-Tracking Based Visualizations and Metrics Analysis for Individual Eye Movement Patterns

机译：基于眼动追踪的可视化和针对个体眼动模式的指标分析

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Uniqueness in the analysis pattern of objects by individual humans has a profound impact on the study of their visual learning and behavior. Eye movement patterns have been effectively emerging as a biometric based key for security systems, product recognition patterns, user identifications, as well as medical research purposes. The modern eye tracking systems are non-invasive and financially affordable. Therefore, in this paper, we proposed eye-tracking based visualizations and metrics analysis for individual eye movement patterns collected during any kinds of activities depending on the scope of the our experimental paradigms. Individuals can be aware of their own performances during certain task and improve upon their weak areas. The objective of the paper is to utilize the important visual metrics obtained from fixation, saccades and face recognition and use them to analyze for individual categorization. The obtained results shown that the specific features and patterns can be extracted the viewing aspect of individual subjects using naive Bayes classifier. We were successfully able to predict the individual eye movements with an accuracy of 90.22%.
机译：单个人的对象分析模式的独特性对他们的视觉学习和行为的研究产生了深远的影响。眼动模式已经有效地发展为基于生物特征的安全系统，产品识别模式，用户标识以及医学研究目的的密钥。现代的眼动追踪系统是非侵入性的，并且在经济上可以承受。因此，在本文中，我们提出了基于眼动图的可视化和度量分析，用于根据我们的实验范式的范围，对在任何类型的活动中收集的个体眼动模式进行分析。个人可以在某些任务中意识到自己的表现，并改善自己的弱点。本文的目的是利用从注视，扫视和面部识别获得的重要视觉指标，并使用它们来分析个人分类。获得的结果表明，可以使用朴素的贝叶斯分类器从各个对象的观看方面提取特定的特征和模式。我们成功地以90.22％的准确度预测了单个眼睛的运动。

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