# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 3 Materials and Methods
### Materials
Classic machine learning and deep learning approaches on the CASME II dataset encounter significant difficulties when directly applied to micro-expressions for the following reasons:  
1. Limited dataset: with only 247 examples and an unbalanced distribution, deep learning models cannot efficiently generalize;
2. AU specificity: psychological research has established clear correspondences between certain AUs and specific emotions;
3. Need for interpretability: in critical applications, it is essential to be able to explain why the system classified a particular expression. For these reasons, the system implements a comprehensive set of expert rules based on specialized literature in emotion psychology and the FACS system. These rules define distinctive AU combinations for each emotion and are essential for resolving ambiguities, as shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Action Unit** & **Description** \\ \hline AU1 & “browInnerUp”, “browInnerUpLeft”,”browInnerUpRight”, “browInnerRaiser” \\ \hline AU2 & “browOuterUpLeft”, “browOuterUpRight” \\ \hline AU4 & “browDownn”, “browDownLeft”, “browDownRight”, “browLower”, “browFurrower” \\ \hline AU5 & “eyeLookUpLeft”, “eyeLookUpLeft”, “eyeLookUpLeft”, “eyeLookUpRight”, “eyeWideLeft”, “eyeWideRight”, “upperLidRaiser” \\ \hline AU6 & “cheekSquintLeft”, “cheekSquintRight”, “cheekRaiser” \\ \hline AU7 & “lidTightener”, “eyeSquintLeft”, “eyeSquintRight” \\ (Crucial for Regression) & “noseSneerLeft”, “noseSneerRight”, “noseWrinkler” \\ \hline AU10 & “mouthUpperUpLeft”, “mouthUpperUpRight”, “upperLipRaiser” \\ \hline AU12 & “lipCornerPuller”, “lipCornerPullerLeft”, “lipCornerPullerRight”, “mouthSmileLeft”, “mouthSmileRight” \\ \hline AU14 & “mouthDimpleLeft”, “mouthDimpleRight” \\ \hline AU15 & “lipCornerDepressor”, “lipCornerDepressorLeft”, “lipCornerDepressorRight”, “mouthFrownLeft”, “mouthFrownRight” \\ \hline AU16 & “mouthLowerDownLeft”, “mouthLowerDownRight” \\ \hline AU17 & “chinRaiser” \\ \hline AU20 & “mouthStretchLeft”, “mouthStretchRight”, “lipStretcher” \\ \hline AU23 & “lipsTogether” \\ \hline AU24 & “lipPressLeft”, “lipPressRight” \\ \hline AU25 & “lipsPart” \\ \hline AU26 & “jawOpen”, “mouthOpen” \\ \hline AU45 & “eyeBlinkLeft”, “eyeBlinkRight” \\ \hline AU29 & “jawForward” \\ \hline AU28 & “jawLeft”, “jawRight” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Action units and their descriptions (Adapted from freely available resource [42]). A key contribution of the proposed system is the implementation of specialized decision rules designed to address common emotional confusions, as illustrated in the block diagram presented in Figure 3.  
The rules are applied hierarchically, first checking the \"must_have\" conditions, then \"should_have\", and finally \"may_have\", ensuring a robust classification even for partial or ambiguous facial micro-expressions. Figure 3: Rules-based logic diagram for eliminating confusions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Emotion** & **AU** \\ \hline Happiness & AU12, AU6 \\ Disgust & AU9, AU10, AU15 \\ Repression & AU4, AU7, AU23 \\ Fear & AU1, AU2, AU4, AU20 \\ Surprise & AU1, AU2, AU5, AU26 \\ Sadness & AU1, AU15, AU17 \\ Contempt & AU14, AU24 \\ Anger & AU 4, AU5, AU7, AU23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Combination of emotions and AUs [6; 43]. Amplification of Weak Signals  
Micro-expressions are characterized by low intensity, which can lead to the incomplete detection of AUs. To compensate for this limitation, the system implements a selective amplification strategy for weak signals.