论文标题
在ABAW3挑战中的多个情感描述符估计
Multiple Emotion Descriptors Estimation at the ABAW3 Challenge
论文作者
论文摘要
为了描述复杂的情绪状态，心理学家提出了多种情感描述：稀疏的描述符，例如面部动作单位；连续描述符，例如价和唤醒；以及像幸福和愤怒这样的离散阶级描述符。根据埃克曼（Ekman）和弗里森（Friesen）的说法，1969年，面部动作单元是传达情感信息的标志工具，而离散或连续的情感描述符是人类所感知和表达的信息。 在本文中，我们为参与ABAW3挑战的多种情感描述估计设计了一个体系结构。根据Ekman和Friesen的理论，1969年，我们设计了独特的架构来测量标志车辆（即面部动作单元）和鉴于其不同的特性，信息（即离散的情绪，价和唤醒）。 ABAW3挑战数据集上的定量实验表明，我们的方法在两个基线模型上的出色性能。
To describe complex emotional states, psychologists have proposed multiple emotion descriptors: sparse descriptors like facial action units; continuous descriptors like valence and arousal; and discrete class descriptors like happiness and anger. According to Ekman and Friesen, 1969, facial action units are sign vehicles that convey the emotion message, while discrete or continuous emotion descriptors are the messages perceived and expressed by human. In this paper, we designed an architecture for multiple emotion descriptors estimation in participating the ABAW3 Challenge. Based on the theory of Ekman and Friesen, 1969, we designed distinct architectures to measure the sign vehicles (i.e., facial action units) and the message (i.e., discrete emotions, valence and arousal) given their different properties. The quantitative experiments on the ABAW3 challenge dataset has shown the superior performance of our approach over two baseline models.
