135-class Emotional Facial Expression Dataset

The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., happiness, anger and surprise) or abstract dimensions (valence, arousal, etc.), while neglecting the fruitful nature of emotion statements. In real-world scenarios, there is definitely a larger vocabulary for describing human inner feelings as well as their reflection on facial expressions. This dataset addresses the semantic richness issue in the FER problem, with an emphasis on the granularity of the emotion concepts. Particularly, we take inspiration from former psycho-linguistic research, which conducted a prototypicality rating study and chose 135 emotion names from hundreds of English emotion terms.Based on the 135 emotion categories, the dataset collects a large-scale 135-class FER image dataset. The paper [1] demonstrates the accessibility of prompting FER research to a fine-grained level by conducting extensive evaluations on the dataset credibility and the accompanying baseline classification model. To the best of our knowledge, this is the first dataset aimed at exploiting such a large semantic space for emotion representation in the FER problem.[1] K. Chen, X. Yang, C. Fan, W. Zhang and Y. Ding, "Semantic-Rich Facial Emotional Expression Recognition," in IEEE Transactions on Affective Computing, vol. 13, no. 4, pp. 1906-1916, 1 Oct.-Dec. 2022, doi: 10.1109/TAFFC.2022.3201290.

感知人类面部情感的能力是多种多模态应用不可或缺的关键特性，尤其是在智能人机交互（HCI）领域。在过去的几十年中，研究人员投入了大量精力研究自动面部情感识别（FER）。然而，现有的多数FER方法仅聚焦于基本情感类别，如七类或八类基本情感（例如快乐、愤怒和惊讶）或抽象维度（如效价、唤醒度等），而忽视了情感表述的丰富内涵。在现实场景中，描述人类内心感受及其在面部表情上的反映的词汇量显然更为庞大。本数据集旨在解决FER问题中的语义丰富性问题，并着重于情感概念的细粒度。特别是，我们借鉴了早期的心理语言学研究成果，该研究通过对数百个英语情感术语进行典型性评级，并从中选择了135个情感名称。基于这135个情感类别，本数据集收集了一个大规模的135类FER图像数据集。论文[1]通过对数据集的可信度和伴随的基线分类模型进行了广泛的评估，证明了将FER研究引导至细粒度水平的可行性。[1] K. Chen, X. Yang, C. Fan, W. Zhang and Y. Ding, "Semantic-Rich Facial Emotional Expression Recognition," in IEEE Transactions on Affective Computing, vol. 13, no. 4, pp. 1906-1916, 1 Oct.-Dec. 2022, doi: 10.1109/TAFFC.2022.3201290.