Facial Landmark Visualization and Emotion Recognition Through Neural Networks

TL;DR

This paper introduces a visualization technique for facial landmark analysis and compares feature sets for emotion recognition, demonstrating neural networks outperform traditional classifiers.

Contribution

It proposes facial landmark box plots for dataset analysis and compares absolute versus displacement features for improved emotion recognition.

Findings

Neural networks outperform random forest classifiers.

Facial landmark box plots help identify dataset outliers.

Displacement features yield better emotion recognition accuracy.

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.