# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 6 Conclusions  
This study presents a significant advancement in the field of micro-expression recognition by introducing a hybrid system that effectively bridges domain-specific knowledge with state-of-the-art artificial intelligence. Through the integration of facial action coding principles, modern computer vision frameworks, and the contextual reasoning capabilities of large language models, the proposed approach offers a novel solution to many of the challenges traditionally associated with micro-expression detection. Specifically, we proposed a hybrid AU-LLM architecture that integrates a rule-based AU detection pipeline with a transformer-based LLM, specifically GPT-3.5-turbo, which serves as an intelligent fallback for ambiguous or partial AU configurations. To our knowledge, this represents one of the first practical implementations of an LLM in a micro-expression recognition system. The system incorporates a rule validation layer that enforces domain-specific AU-emotion mappings after LLM classification, ensuring that the outputs remain interpretable and conform to psychological ground truths--a functionality often missing in black-box hybrid models. Additionally, we demonstrated robust performance on the CASME II dataset, achieving 93.3% accuracy despite severe class imbalance, without relying on conventional deep model retraining or synthetic data generation. This was enabled by our AU-based amplification strategy, which enhanced subtle expression patterns in a lightweight, interpretable way. Finally, our use of transparent prompt engineering allowed the LLM to operate within well-defined diagnostic constraints, promoting explainability and domain alignment rather than generic text-based reasoning. These integrated innovations collectively position our system as a novel, scalable, and explainable solution for real-time micro-expression analysis. Theoretical insights gained from this work reinforce the importance of AU patterns in encoding emotional content, and the system's confusion matrix analysis revealed empirically grounded trends in misclassification between visually similar emotions. These findings contribute to a deeper understanding of the perceptual and semantic overlap among certain emotional states. Furthermore, the observed variability in expression patterns across subjects underscores the necessity of adaptive mechanisms that account for individual differences in affective display. The study also highlights the role of contextual interpretation, suggesting that purely visual analysis may be insufficient for robust emotion inference, especially in subtle or ambiguous expressions. From a practical standpoint, the developed system holds considerable promise for real-world applications across several domains. In security and law enforcement, it offers a tool for enhancing deception detection and behavioral analysis. In clinical psychology, it provides an objective layer for evaluating patient affect, potentially supporting diagnostic and therapeutic processes. Marketing and consumer research may benefit from its capacity to discern authentic emotional reactions, while human-computer interaction stands to gain from the system's potential to enable emotionally responsive interfaces. By demonstrating the effectiveness of combining symbolic rules, weak signal amplification, and AI-driven classification, this work lays a foundation for more intelligent, interpretable, and adaptable emotion recognition systems. It also opens pathways for further exploration into multimodal emotion analysis, cultural variation in expression, and real-time deployment in diverse environments. In doing so, the study contributes meaningfully to the broader goals of affective computing and emotionally intelligent technologies. While the current system achieved strong performance and demonstrated real-time feasibility, several avenues remain open for future exploration.