Detecting Children Emotion through Facial Expression Recognition

Emotion detection has become an increasingly important area of research in recent years, as it has numerous applications in fields such as psychology, marketing, and human-computer interaction. Deep learning has shown success in emotion recognition due to the availability of large amounts of data and the ability to learn complex patterns in facial expressions, speech, and physiological signals when recognizing emotions. However, there are challenges associated with variations in lighting, pose, and facial expressions. This study introduces a novel deep-learning approach for emotion detection, leveraging the power of VGGFace2 to classify six emotional poses in children. The proposed approach outperforms the state-of-the-art in the field, achieving a success rate of 96.3% on the CAFE dataset. A comprehensive evaluation of the findings and a detailed discussion of their potential implications is offered as part of the study. Facial Emotion Recognition (FER), VGGFace2, emotion detection