# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Type** & **Accuracy (\%)** & **Main Advantage** & **Limitation** \\ \hline
3D CNN + Apex Frame Selection [45] & 88.2 & Strong deep learning baseline with optimized video selection & Less interpretable, requires large data \\ \hline Hybrid CNN (DLHN) + Handcrafted Features [48] & 60.3 & Good balance of learned and handcrafted features & Lower accuracy on fine-grained classes \\ \hline Video Transformer + Optical Flow [47] & 73.2 & Transformer model for subtle motion capture & Computationally intensive, comparable accuracy only \\ \hline Shallow CNN (ResNet-optimized) & 60.2 & Fast, low-resource, real-time compatible & Lower accuracy, limited class resolution \\ + Optical Flow [46] & 90.34 & State-of-the-art micro-expression accuracy using unsupervised pretraining & High computational demand, less interpretability, needs \\ \hline Multimodal LLM (GPT-4V), zero-shot prompting [24] & 14.64 & Zero-shot capability across multiple GER tasks, strong multimodal reasoning & Low accuracy in micro-expression tasks due to lack of specialized knowledge \\ \hline Our modular system + OpenAI API + Expert Rules & 93.3 & High accuracy, interpretable, handles emotional confusion & Requires prompt design and external API \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis. and Patch of Interest. In contrast, our use of domain-specific rules and prompt optimization makes the system more transparent and adaptable to practical settings with limited computational resources. The GPT-4V model evaluated by [24] demonstrates the growing potential of multimodal large language models (MLLMs) in generalized emotion recognition tasks. However, its low accuracy (14.64%) on micro-expression recognition highlights a clear gap between general-purpose zero-shot models and domain-specialized systems. This result emphasizes the importance of incorporating domain knowledge--either through expert rules, task-specific architecture (as in \(\upmu\)-BERT), or hybrid approaches like ours--to handle subtle, low-intensity emotional expressions effectively. While LLMs enhance the system's adaptability, domain-informed classification based on action units remains essential for high-precision tasks. The hybrid integration ensures reliable performance in sensitive applications where both accuracy and interpretability are critical. The findings of this study offer several important advancements in the field of micro-expression recognition, with implications for both scientific understanding and real-world applications. The proposed hybrid architecture--combining MediaPipe-based facial feature extraction, expert rule encoding, and prompt-driven language model classification--represents a novel paradigm that departs from traditional end-to-end deep learning pipelines. This modular integration enhances system interpretability and enables flexible adaptation across datasets and use cases. One of the most notable contributions lies in the resolution of frequent emotional confusions such as those between fear and surprise, or sadness and disgust. By embedding specialized decision rules into the classification logic, the system effectively addresses a persistent challenge in affective computing: the overlap in facial AUs between certain emotion classes. This targeted disambiguation, supported by high recall and precision scores, underpins the system's superior accuracy compared with both conventional CNN-based and transformer-based architectures. Moreover, the inclusion of weak signal amplification techniques addresses the inherent difficulty of detecting micro-expressions--brief, subtle, and low-intensity facial movements often imperceptible to the human eye. This capability positions the system for high sensitivity in security-critical applications, such as lie detection or psychological assessment, where missed signals could be consequential. The study also introduced a continuous learning framework, allowing the knowledge base to evolve as new data are encountered.