A Method for Automatic Emotion Detection Through Machine Learning

Facial expression recognition (FER) is a fundamental component of Affective Computing and is gaining increasing relevance in mental health applications. This study presents an approach for facial expression recognition using feature extraction and machine learning techniques. Starting from a publicly available dataset, a manual cleaning and relabeling process led to the creation of a refined dataset of 35,625 facial images grouped into four emotional macroclasses. Features were extracted using the SqueezeNet and Inception v3 embedders and classified using various algorithms. The experimental results show that Inception v3 consistently outperforms SqueezeNet and that feature normalization improves classification stability and robustness. The results highlight the importance of data quality and preprocessing in applied FER systems.