Emotion recognition from facial images is a crucial task in human-computer interaction, enabling machines to learn human emotions through facial expressions. Previous studies have shown that facial images can be used to train deep learning models; however, most of these studies do not include a through dataset analysis. Visualizing facial landmarks can be challenging when extracting meaningful dataset insights; to address this issue, we propose facial landmark box plots, a visualization technique designed to identify outliers in facial datasets. Additionally, we compare two sets of facial landmark features: (i) the landmarks' absolute positions and (ii) their displacements from a neutral expression to the peak of an emotional expression. Our results indicate that a neural network achieves better performance than a random forest classifier.
View on arXiv@article{juárez-jiménez2025_2506.17191,
title={ Facial Landmark Visualization and Emotion Recognition Through Neural Networks },
author={ Israel Juárez-Jiménez and Tiffany Guadalupe Martínez Paredes and Jesús García-Ramírez and Eric Ramos Aguilar },
journal={arXiv preprint arXiv:2506.17191},
year={ 2025 }
}