🤖 AI Summary
This study addresses facial emotion recognition in human-computer interaction, focusing on data quality assessment and feature representation optimization. To this end, we propose a novel boxplot-based visualization method leveraging facial landmarks for outlier detection in facial datasets—the first such application. We systematically compare absolute-coordinate landmarks against neutral-to-peak displacement features, providing the first empirical evidence that displacement features significantly outperform absolute coordinates in emotion classification. Landmarks are extracted using dlib and MMPose; classification is performed via CNN and Random Forest models. Results demonstrate that CNN substantially surpasses Random Forest in accuracy; moreover, displacement features markedly enhance model robustness and cross-dataset generalization. This work contributes an interpretable, landmark-driven data quality control tool and establishes a superior feature paradigm—displacement-based representation—for facial emotion recognition.
📝 Abstract
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.