| Resumen |
Emotion detection using computer vision has advanced significantly in recent years, achieving remarkable performance that, in some cases, surpasses that of humans. Convolutional neural networks (CNNs) excel in this task by capturing facial features that allow for effective emotion classification. However, most research focuses on basic emotions, such as happiness, anger, or sadness, neglecting more complex emotions, like frustration. People set expectations or goals to meet; if they do not happen, frustration arises, generating reactions such as annoyance, anger, and disappointment, which can harm confidence and motivation. These aspects make it especially relevant in mental health and educational contexts, where detecting it could help mitigate its adverse effects. In this research, we developed a CNN-based approach to detect frustration through facial expressions. The scarcity of specific datasets for this task led us to create an experimental protocol to generate our dataset. This classification task presents a high degree of difficulty due to the variability in facial expressions among different participants when feeling frustrated. Despite this, our new model achieved an F1-score of 0.8080, thus obtaining an adequate baseline model. |
