Facial emotion recognition has been typically cast as a single-label classification problem of one out of six prototypical emotions. However, that is an oversimplification that is unsuitable for representing the multifaceted spectrum of spontaneous emotional states, which are most often the result of a combination of multiple emotions contributing at different intensities. Building on this, a promising direction that was explored recently is to cast emotion recognition as a distribution learning problem. Still, such approaches are limited in that research datasets are typically annotated with a single emotion class. In this paper, we contribute a novel approach to describe complex emotional states as probability distributions over a set of emotion classes. To do so, we propose a solution to automatically re-label existing datasets by exploiting the result of a study in which a large set of both basic and compound emotions is mapped to probability distributions in the Valence-Arousal-Dominance (VAD) space. In this way, given a face image annotated with VAD values, we can estimate the likelihood of it belonging to each of the distributions, so that emotional states can be described as a mixture of emotions, enriching their description, while also accounting for the ambiguous nature of their perception. In a preliminary set of experiments, we illustrate the advantages of this solution and a new possible direction of investigation. Data annotations are available at https://github.com/jbcnrlz/affectnet-b-annotation.

翻译：面部情绪识别通常被构建为从六种原型情绪中选择其一的单标签分类问题。然而，这种过度简化的方法不足以表征自发性情绪状态的多维谱系，后者往往是多种情绪以不同强度组合产生的结果。基于此，近期探索的一个有前景方向是将情绪识别构建为分布学习问题。尽管如此，此类方法仍存在局限，因为研究数据集通常仅标注单一情绪类别。本文提出了一种新颖方法，将复杂情绪状态描述为在情绪类别集合上的概率分布。为此，我们提出一种解决方案，通过利用一项大规模研究的结果自动重新标注现有数据集——该研究将基础情绪与复合情绪映射至效价-唤醒-支配度（VAD）空间中的概率分布。基于此方法，给定标注有VAD值的人脸图像，我们可以估计其属于每个分布的可能性，从而使情绪状态能够被描述为多种情绪的混合体。这不仅丰富了情绪状态的描述维度，同时也考虑了情绪感知的模糊性本质。通过初步实验，我们展示了该解决方案的优势以及新的潜在研究方向。数据标注可通过 https://github.com/jbcnrlz/affectnet-b-annotation 获取。