# An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset

arXiv: https://arxiv.org/abs/2304.03064 · HTML: https://mlsanity.com/paper/2304.03064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 18 of 18)

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- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
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- [Anomaly detection optimization using big data and deep learning to reduce false-positive](https://mlsanity.com/paper/2209.13965/) (2022)
- [Deep Learning For Smile Recognition](https://mlsanity.com/paper/1602.00172/) (2016)
- [Multi-context Attention Fusion Neural Network for Software Vulnerability Identification](https://mlsanity.com/paper/2104.09225/) (2021)

## Abstract (arXiv)

Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of the-art available datasets still suffer from various problems, such as some unrelated photos such as document photos, unbalanced numbers of photos in each class, and misleading images that can negatively affect correct classification. The 3RL dataset was created, which contains approximately 24K images and will be publicly available, to overcome previously available dataset problems. The 3RL dataset is labelled with five basic emotions: happiness, fear, sadness, disgust, and anger. Moreover, we compared the 3RL dataset with other famous state-of-the-art datasets (FER dataset, CK+ dataset), and we applied the most commonly used algorithms in previous works, SVM and CNN. The results show a noticeable improvement in generalization on the 3RL dataset. Experiments have shown an accuracy of up to 91.4% on 3RL dataset using CNN where results on FER2013, CK+ are, respectively (approximately from 60% to 85%).