No abstract

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 60–85%).

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 like document photos, unbalanced number of photos in each class, and some misleading images that can affect negatively on correct classification. 3RL dataset was created which contains about 24K images and will be publically available, to overcome previously available datasets problems. 3RL dataset is labelled with five basic emotions: happiness, fear, sadness, disgust, and anger. Moreover, we compared 3RL dataset with other most famous state-of-the-art datasets (FER dataset, CK+ dataset), we have applied the most common used algorithms in previous works, SVM and CNN. Results have shown a noticeable improvement of generalization on 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%).