Facial Landmark Visualization and Emotion Recognition Through Neural Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909654374678528 |
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| author | Juárez-Jiménez, Israel Paredes, Tiffany Guadalupe Martínez García-Ramírez, Jesús Aguilar, Eric Ramos |
| author_facet | Juárez-Jiménez, Israel Paredes, Tiffany Guadalupe Martínez García-Ramírez, Jesús Aguilar, Eric Ramos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Facial Landmark Visualization and Emotion Recognition Through Neural Networks Juárez-Jiménez, Israel Paredes, Tiffany Guadalupe Martínez García-Ramírez, Jesús Aguilar, Eric Ramos Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Facial Landmark Visualization and Emotion Recognition Through Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17191 |