# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 5 Discussion  
The results of our micro-expression detection system demonstrate a strong overall performance, particularly in recognizing high-frequency emotions such as fear, disgust, and repression. The system achieved a high-weighted average across the key evaluation metrics, reflecting its ability to handle class imbalance effectively. This indicates that the model is well-calibrated for real-world applications where emotional expressions are often unevenly distributed. The consistently high recall rates across most categories highlight the system's sensitivity--an essential feature for detecting fleeting micro-expressions in dynamic environments. Notably, the system excelled in identifying fear, with near-perfect metrics, suggesting that its associated facial action patterns are both distinct and well-learned by the model. Sim  
Figure 6: Real-time running results (presented with the written approval of the subject). ilarly, the reliable performance in disgust and repression detection reinforces the system's robustness for these categories. However, a different pattern emerged for less represented or more ambiguous emotions. For example, while sadness showed perfect recall, its low precision indicates a high false-positive rate, largely due to its confusion with disgust. This misclassification reflects underlying similarities in facial muscle activity between these emotions, a challenge compounded by the limited number of sadness samples available for training. Furthermore, the confusion between happiness and surprise--despite high precision for the former--suggests overlapping expressive features, likely due to shared AU activations around the mouth and eyes. Similar ambiguities were observed in the \"Others\" category, which, although handled with high precision, suffered from moderate recall, implying that several non-standard expressions were misassigned to more defined emotional classes. With 93.3% accuracy on CASME II under the class imbalance and overlapping categories, the architecture demonstrated strong robustness--driven by expert-rule integration and AU-based reasoning. The comparative analysis presented in Table 5 highlights both the diversity of the methodologies used in micro-expression recognition and the relative strengths of our proposed system. Importantly, all listed methods were evaluated on the CASME II dataset, which ensures the comparability of results under consistent experimental conditions. Within this shared benchmark, our modular architecture--combining expert-rule reasoning with GPT-3.5-based prompt engineering--achieved the highest reported accuracy of 93.3%. This performance exceeded that of traditional deep learning models such as 3D CNNs [45], lightweight ResNet-based architectures [46], and video transformers [47] as well as advanced transformer-based methods like \(\mu\)-BERT [23], which currently represents one of the most effective approaches tailored for micro-expression recognition. The consistent dataset context confirms that the performance gains are attributable to the architecture itself, rather than to differences in data or evaluation protocol. Compared with \(\mu\)-BERT, our method delivered a comparable performance (90.34% vs. 93.3% accuracy), while offering greater interpretability and reduced computational complexity. \(\mu\)-BERT's architecture, although highly accurate, relies on extensive pretraining on millions of unlabeled frames and specialized modules such as Diagonal Micro-Attention