# Facial Landmark Visualization and Emotion Recognition Through Neural Networks

arXiv: https://arxiv.org/abs/2506.17191 · HTML: https://mlsanity.com/paper/2506.17191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [I Know How You Feel: Emotion Recognition with Facial Landmarks](https://mlsanity.com/paper/1805.00326/) (2018)
- [Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks](https://mlsanity.com/paper/2202.00102/) (2022)

## Abstract (arXiv)

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.