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

机译：基于眼睛跟踪的可视化和单个眼睛运动模式的度量分析

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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：个人人类对物体的分析模式中的唯一性对他们的视觉学习和行为的研究产生了深刻的影响。眼部运动模式已被有效地作为安全系统，产品识别模式，用户识别以及医学研究目的的基于生物识别的键。现代眼追踪系统是无侵入性和经济实惠的。因此，在本文中，我们提出了根据我们的实验范式范围的范围内的任何类型的活动中收集的个体眼睛运动模式的基于眼睛运动模式的简历可视化和度量分析。个人可以在某项任务期间了解自己的表现，并改善他们的弱势地区。本文的目的是利用从固定，扫描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。所获得的结果表明，使用Naive Bayes分类器可以提取特定特征和图案的各个主体的观察方面。我们成功地预测了以90.22％的准确性预测单个眼睛运动。

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