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