This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies. Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence. A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method.

中文翻译：

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眼动分析，用于情感识别。

本文报告了有关使用眼动追踪进行情绪识别的研究结果。通过以21个视频片段的形式呈现动态电影材料，唤起了人们的情感。从30位参与者记录的眼动信号用于计算与眼动（固定和扫视）和瞳孔直径相关的18个特征。为了确保这些功能与情感相关，我们研究了亮度和所呈现电影动态变化的影响。考虑了三类情绪：高唤醒和低价，低唤醒和中价，高唤醒和高价。使用支持向量机（SVM）分类器和留一法验证方法可获得最高80％的分类精度。