# Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition
## 4 Experiment and Analysis
### Detail of Experiment and Analysis  
Our experiments use LOSO (Leave-One-Subject-Out) as the evaluation standard and use the accuracy and F1-score as the performance indexes of micro-expression recognition.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Figure** \\ \hline epoch & 50 \\ batch\_size & 32 \\ base\_lr & \(1\times 10^{-5}\) \\ decay & \(1\times 10^{-7}\) \\ momentum & 0.9 \\ random deactivation rate & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of our network.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Emotions** & **SMIC** & **Processed** & **Emotions** & **CASME II** & **Processed** \\ \hline \multirow{3}{*}{Positive} & \multirow{3}{*}{51} & \multirow{3}{*}{1020} & Happiness & 32 & 640 \\  & & & Surprise & 25 & 500 \\  & & & Disgust & 63 & 1260 \\ \multirow{3}{*}{Negative} & \multirow{3}{*}{70} & \multirow{3}{*}{1400} & \multirow{3}{*}{Repression} & 27 & 540 \\  & & & Sadness & 7 & - \\ \multirow{3}{*}{Surprise} & \multirow{3}{*}{43} & \multirow{3}{*}{860} & Fear & 2 & - \\  & & & Others & 99 & 1980 \\ \multirow{3}{*}{Total Size} & \multirow{3}{*}{164} & \multirow{3}{*}{3280} & Total Size & 255 & 4920 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample distribution of datasets.  
By reserving a specific participant sample as the test sample and using other participant samples as the training sample, we divide the micro-expression recognition accuracy of all participants equally. The final accuracy is \((\sum_{i=1}^{n}c_{i})/n\), and the F1-score is \((\sum_{i=1}^{n}f_{i})/n\).  
The calculation equation of accuracy is shown in Equation (12):  
\[\text{Accuracy}\ =(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{12}\]  
The F1-score is defined as Equation (13):  
\[\left\{\begin{array}{l}P=\frac{TP}{TP+FP}\\ R=\frac{TP}{TP+FN}\\ F1=\frac{2*P*R}{P+K}\end{array}\right. \tag{13}\]  
We compared four traditional methods and three methods based on deep learning with excellent performance in the past few years on the SMIC and CASME II datasets. Our method achieved the best results in accuracy and F1-score among these methods, which shows that it has significant advantages in micro-expression recognition. The specific experimental results are shown in Table 5.  
The proposed method outperforms other methods in recognition performance on both the CASME II and SMIC datasets. This is because our proposed method does not use traditional RGB images but instead utilizes video motion to recognize micro-expressions, thereby better capturing the dynamics of expressions. Additionally, we combine spatiotemporal characteristics with static features, modeling micro-expressions from multiple perspectives in a fine-grained manner.  
DSSN captures micro-expressions from different angles but only focuses on micro-expression changes at the image level without considering optical flow features and dynamic changes. MDMO divides the face into 36 non-overlapping regions and extracts optical flow features frame by frame. However, it only applies optical flow features, neglecting the continuous spatiotemporal changes in micro-expressions, resulting in less effective performance compared to our model.