# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 1 Introduction  
Micro-facial expressions are brief, involuntary facial movements that occur within \(1/25\) to \(1/15\) of a second and reveal genuine emotional states that individuals may attempt to conceal [1]. These subtle cues are of particular interest in emotionally charged contexts such as deception, internal conflict, or suppressed reactions. Despite their brevity and low intensity, micro-expressions are recognized as powerful indicators of emotional authenticity, with significant implications across various fields including clinical psychology, law enforcement, neuropsychiatry, marketing, and human-computer interaction. In clinical settings, micro-expression analysis can help therapists assess patient responses more objectively. In security applications, these expressions may aid in the detection of suspicious or deceptive behavior. Similarly, in market research and advertising, understanding subtle consumer reactions can refine product design and campaign strategies. Within human-computer interaction, recognizing micro-expressions enables systems to respond more empathetically and effectively to user states. Additionally, in cognitive and affective neuroscience, studying these involuntary signals provides insights into emotional processing and regulation mechanisms [2]. However, the automatic recognition of micro-expressions presents several challenges. These include their extremely short duration, often lasting under 100 milliseconds; the low amplitude of facial muscle movements, typically two to five times smaller than standard expressions; high inter-subject variability due to differences in facial morphology and expression patterns; ambiguity in emotional interpretation; and frequent confusion between visually similar expressions such as fear and surprise or sadness and disgust. Moreover, the scarcity of well-annotated training data and the absence of contextual cues further complicate the accurate detection and classification of micro-expressions [3]. The CASME II dataset [4] was developed with the specific aim of supporting micro-expression research. It comprises 247 high-speed video sequences (200 FPS) from 35 subjects and includes detailed annotations such as emotion categories, facial action units (AUs), and onset-apex-offset frames. The dataset's design--eliciting genuine but suppressed emotional responses under controlled stimuli--makes it particularly suited for developing systems that simulate real-world micro-expression analysis conditions. To address the existing limitations in micro-expression recognition, this research introduces a hybrid real-time detection system that integrates psychological models, like Facial Action Coding System (FACS), computer vision (MediaPipe-based facial landmark detection), and artificial intelligence (AI)--specifically, large language models (LLMs) such as OpenAI's GPT-3.5--for emotion classification. This system bridges the interpretability and rule-driven precision of classical methods with the generalization power of data-driven AI, yielding a model that is both robust and adaptable in dynamic environments. Our approach is grounded in several innovations. First, it introduces a modular architecture that enhances interpretability and facilitates component-level optimization. Second, it leverages expert-defined rules to resolve known emotional confusions such as those between fear and surprise or sadness and disgust. Third, it incorporates weak signal amplification mechanisms to better capture low-intensity expressions, and fourth, it implements mechanisms for continuous learning, enabling the system to update its knowledge base as it encounters new subjects. These contributions are validated through rigorous experiments on the CASME II dataset using leave-one-subject-out and k-fold cross-validation protocols. In addition to technical contributions, this work explores an emerging interdisciplinary frontier: the application of LLMs in affective computing.