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To describe complex emotional states, psychologists have proposed multipleemotion descriptors: sparse descriptors like facial action units; continuousdescriptors like valence and arousal; and discrete class descriptors likehappiness and anger. According to Ekman and Friesen, 1969, facial action unitsare sign vehicles that convey the emotion message, while discrete or continuousemotion descriptors are the messages perceived and expressed by human.  In this paper, we designed an architecture for multiple emotion descriptorsestimation in participating the ABAW3 Challenge. Based on the theory of Ekmanand Friesen, 1969, we designed distinct architectures to measure the signvehicles (i.e., facial action units) and the message (i.e., discrete emotions,valence and arousal) given their different properties. The quantitativeexperiments on the ABAW3 challenge dataset has shown the superior performanceof our approach over two baseline models.

Multiple Emotion Descriptors Estimation at the ABAW3 Challenge