# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 5 Discussion
This adaptability enhances generalization to new subjects and mitigates the risk of overfitting to limited or imbalanced datasets such as CASME II. Furthermore, the use of rigorous validation protocols, including leave-one-subject-out and k-fold cross-validation, ensures the robustness and reproducibility of the results. In sum, this research contributes by offering a methodologically transparent, high-performance, and practically deployable system for micro-expression recognition. It bridges the gap between high interpretability and cutting-edge accuracy while also setting a precedent for how hybrid AI-human knowledge systems can outperform purely data-driven approaches in specialized affective computing tasks. While our system addresses several critical limitations of traditional micro-expression recognition methods, such as extending recognition to seven distinct emotions, handling short-duration and low-intensity expressions, functioning under variable lighting and head pose conditions, and adapting to individual expression differences, there remain important challenges that warrant discussion. First, despite the system's demonstrated effectiveness, it is still partially dependent on the quality and diversity of the training dataset. The CASME II dataset, though widely used, presents a known class imbalance, with emotions such as sadness and surprise being significantly underrepresented. This imbalance may have introduced classification bias, especially in rare emotion categories, leading to inflated recall but lower precision, as observed in the case of sadness. The rule-based disambiguation partially mitigated these effects but could not fully compensate for the lack of representative data. Second, while the integration of expert rules and prompt-based language models significantly improved interpretability and performance, the system's reliance on handcrafted rules and prompt engineering introduces potential brittleness. The effectiveness of emotion disambiguation heavily depends on the quality and comprehensiveness of these rules. As such, the system may not generalize optimally to unseen emotion variants or spontaneous expressions that fall outside the defined decision boundaries. Third, although the system includes mechanisms for continuous knowledge updating, these are not fully autonomous and require manual oversight for rule validation and performance assessment. This semi-automated learning may limit scalability in real-time or high-volume deployment scenarios. Additionally, while robustness to variable lighting and moderate head movements was qualitatively observed, these factors were not explicitly isolated and tested in controlled experimental conditions. Therefore, the claimed generalizability across challenging real-world scenarios, such as occlusion, rapid head motion, or extreme illumination shifts, should be interpreted with caution until further validation is conducted. Finally, while the integration of the OpenAI GPT model enabled sophisticated emotion classification, it introduced a dependence on external API access, associated usage limits, and potential variability in model responses. This reliance could affect system consistency in time-sensitive or privacy-constrained applications. In summary, although the proposed system sets a new benchmark in hybrid micro-expression recognition, future work should address dataset diversity, enhance rule adaptability, automate knowledge updating, and improve robustness testing to further solidify its utility in practical, real-world settings. Building on the promising results and architectural innovations of this study, several directions for future research can be pursued to further advance the field of micro-expression recognition. First, to mitigate class imbalance and improve generalization across rare emotional categories, future studies should incorporate more diverse and balanced datasets including CASME3, SAMM, and SMIC.