Study of Spontaneous and Acted Learn-Related Emotions Through Facial Expressions and Galvanic Skin Response

Abstract

The objective of this work is the reliable recognition of spontaneous and acted emotions related with learning activities through a classificatory off facial expressions and Galvanic Skin Response (GSR). The induction of spontaneous emotions was generated by clips from movies that showed a good capacity to induce positive and negative effects, elevated levels of emotional activation and variations in the perception of emotional control. On the other hand, the emotions performed were replicated by the participants imitating a sequence of illustrations referencing the emotions. The classificatory was applied to 23 participants of Hispanic ethnic, participants were told a series of instructions. The experiments in this work were based in emotion classification by facial expressions through a Convolutional Neural Network and Galvanic Skin Response. Once, obtained the raw data of the experiment, it processed to the training of classificatory from deep learning and statistical analysis. The Convolutional Neural Network presented a validation accuracy of 96.5%, a confusion matrix was made with the images of the data set at random and external data sets. In the confusion matrix of the external data set, the real emotions are confused with others, mainly in happy and boredom, correctly classifying in the stress. In the results of Galvanic Skin Response, percentage change tests were performed between the emotions in which significant different were found in happy and boredom, except in stress. With the support of the Neuronal Convolutional Network and Galvanic Skin Activity, it is possible to have a correct classifier of emotions related to learning activities.