Facial Landmark Visualization and Emotion Recognition Through Neural Networks
This work addresses dataset analysis challenges in facial emotion recognition for human-computer interaction, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of visualizing facial landmarks for emotion recognition by proposing facial landmark box plots to identify dataset outliers and comparing absolute vs. displacement features, resulting in a neural network outperforming a random forest classifier.
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.