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A Novel Technique to Develop Cognitive Models for Ambiguous Image Identification Using Eye Tracker

机译:一种新的技术,用于使用眼跟踪器开发模糊图像识别的认知模型

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摘要

Human behavior can be analyzed using Eye tracker. Thus, it is used for revealing the cognitive processes for object identification. Cognitive process is the mental ability for identification of what our eyes see. Vision with 20/20 sometimes may not reveal the purpose. In this study, ambiguous images are taken to observe the cognitive process in participants. During the perception of an object, a participant uses goal-directed search for identifying various objects. Dense gaze coordinates provide the region of interests and are considered as the target regions for object identification in ambiguous images. These data are used to develop cognitive models for identification of ambiguous images. Features such as, eye fixation, pupil diameter, fixation durations, moments of inertia, and polar moments are used for developing the cognitive model. Three different feature selection methods along with six different classifiers are used for the task of classification. The selection of a subset of features using hypothesis testing performed well, compared to principal component analysis based dimensionality reduction method. This study could be used in detecting whether a participants is lying or not while perceiving an ambiguous image.
机译:可以使用眼跟踪器分析人类行为。因此,它用于揭示对象识别的认知过程。认知过程是鉴定我们眼睛所看到的内容的心理能力。 20/20的愿景有时可能不会透露目的。在这项研究中,采取了模糊的图像来观察参与者中的认知过程。在对象的感知期间,参与者使用目标针对识别各种对象的目标。致密的凝视坐标提供感兴趣的区域,被认为是模糊图像中对象识别的目标区域。这些数据用于开发认知模型以识别模糊图像。使用诸如眼固定,瞳孔直径,固定持续时间,惯性矩和极性时刻的特征用于开发认知模型。三种不同的特征选择方法以及六种不同的分类器用于分类任务。与基于维度减少方法的主要成分分析相比,使用假设检测选择使用假设检测的特征子集。该研究可用于检测参与者是否撒谎,同时感知模糊图像。

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