  Emotion recognition in social situations is a complex task that requires
integrating information from both facial expressions and the situational
context. While traditional approaches to automatic emotion recognition have
focused on decontextualized signals, recent research emphasizes the importance
of context in shaping emotion perceptions. This paper contributes to the
emerging field of context-based emotion recognition by leveraging psychological
theories of human emotion perception to inform the design of automated methods.
We propose an approach that combines emotion recognition methods with Bayesian
Cue Integration (BCI) to integrate emotion inferences from decontextualized
facial expressions and contextual knowledge inferred via Large-language Models.
We test this approach in the context of interpreting facial expressions during
a social task, the prisoner's dilemma. Our results provide clear support for
BCI across a range of automatic emotion recognition methods. The best automated
method achieved results comparable to human observers, suggesting the potential
for this approach to advance the field of affective computing.
