In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models

TL;DR

This paper explores a novel approach to emotion recognition that combines facial expression analysis with contextual knowledge inferred from large language models, demonstrating results comparable to human perception in social scenarios.

Contribution

It introduces a Bayesian Cue Integration framework that effectively combines decontextualized facial cues with contextual knowledge from large language models for emotion recognition.

Findings

BCI improves emotion recognition accuracy across methods

Automated methods achieve performance comparable to humans

Contextual integration enhances emotion perception in social tasks

Abstract

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.