An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset
This work addresses dataset quality issues for researchers in human-computer interaction, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of real-time facial emotion recognition by creating the 3RL dataset to address issues like unrelated photos and class imbalance in existing datasets, achieving up to 91.4% accuracy with CNN, which is higher than results on FER2013 and CK+ datasets (approximately 60% to 85%).
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%).