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%).

翻译：尽管实时面部表情识别是人机交互领域的热门研究方向，但现有公开数据集仍存在各类问题，例如包含文档照片等无关图像、各类别图片数量不均衡，以及可能影响正确分类的误导性图像。为克服现有数据集存在的问题，我们创建了包含约24,000张图片的3RL数据集（将公开提供）。该数据集标注了五种基本情绪：快乐、恐惧、悲伤、厌恶和愤怒。此外，我们将3RL数据集与其它著名先进数据集（FER数据集、CK+数据集）进行对比，并采用前人工作中最常用的SVM和CNN算法。结果表明，3RL数据集在泛化能力方面有显著提升。实验显示，使用CNN在3RL数据集上达到91.4%的准确率，而在FER2013和CK+数据集上的准确率分别为（约60%至85%）。