Work

A Method for Automatic Emotion Detection Through Machine Learning

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app16010397

Authors: Jessica Lucarelli, Mario Cesarelli, Antonella Santone, Fabio Martinelli, Francesco Mercaldo

Institutions: University of Molise, University of Sannio, Institute for High Performance Computing and Networking

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro