# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 5 Results and Discussion
### Ablation Study
#### 5.2.1 Labelling  
The ablation study was conducted on the original IML-based pseudo-labelling and IML-based pseudo-labelling with frame extensions. The effects from the pseudo-labelling were investigated by increasing the \(T_{fEXT}\) parameter linearly according to the \(k\) parameter; that is, the average frames for MEs or MaEs in that particular dataset. The main purpose of this was to analyse the effects of pseudo-labelling and the model's ability to learn from the MHI data provided. Tables 3 and 4 shows the results of the ablation study of a comparison between the Softmax classifier and the Random Forest classifier and frame extension pseudo-labelling. The optical flow input features were experimented with using our architecture to compare them with our MHI input feature. Unlike MHI, optical flow images do not have temporal features that require pseudo-labelling with temporal extensions. Optical flow features were tested using Softmax and Random Forest classifiers without any frame extension labelling. The results in Table 3 show the ME and MaE detection experiment for the CAS(ME)\({}^{2}\) dataset. From Table 3, the results of the ME detection experiment with \(k=6\) for the CAS(ME)\({}^{2}\) dataset show that the MHI method's performance varied significantly with temporal extension settings. Both classifiers showed poor performance without extension labelling, but with temporal extensions, particularly at \(T_{sEXT}=0.167\) s, the Random Forest classifier achieved the best F1-score of 0.3019. In contrast, the Softmax classifier achieved 0.1880 at \(T_{sEXT}=0.100\). This suggests that combining MHI with temporal extensions and Random Forest classification is the most effective approach in this comparative study. Moreover, optical flow input features trained with softmax and Random Forest classifier obtained F1-scores of 0.1529 and 0.2179, respectively, but were significantly lower than our proposed MHI features. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Input**} & \multicolumn{4}{c}{**CAS(ME)\({}^{2}\)**} & \multicolumn{4}{c}{**SAMM-LV**} \\ \cline{3-8}  & & **MaE** & **ME** & **Overall** & **MaE** & **ME** & **Overall** \\ \hline [PERSON] et al. [26] & MDMD & 0.1196 & 0.0082 & 0.0376 & 0.0629 & 0.0364 & 0.0445 \\ [PERSON] et al. [27] & OS & 0.1436 & 0.0098 & 0.0448 & — & — & — \\ [PERSON] et al. [28] & RGB Images & — & — & 0.0595 & — & — & 0.0813 \\ [PERSON] et al. [12] & OF & 0.2131 & 0.0547 & 0.1403 & 0.1331 & 0.0725 & 0.0999 \\ [PERSON] et al. [29] & OF + OS & 0.2410 & 0.1173 & 0.2022 & 0.2169 & 0.1520 & 0.1881 \\ [PERSON] et al. [2] & LCN & 0.2145 & 0.0714 & 0.1675 & 0.1595 & 0.0466 & 0.1084 \\ [PERSON] et al. [3] & OF + OS & 0.4104 & 0.0808 & 0.3620 & 0.3459 & 0.0878 & 0.2867 \\ [PERSON] et al. [17] & OF + OS & 0.2236 & 0.0879 & 0.1834 & 0.1675 & 0.1044 & 0.1357 \\ [PERSON] et al. [6] & OF & 0.4018 & 0.2474 & 0.3838 & 0.3257 & 0.2555 & 0.3211 \\ LGNMNet-RF (Ours) & MHI & 0.3400 & 0.3019 & 0.3594 & 0.4444 & 0.3604 & 0.4118 \\ \hline \end{tabular}
\end{table} 
Table 2: F1-scores \(\uparrow\) comparison of the proposed method and baseline methods. On the other hand, the results of the MaE detection experiment with \(k=18\) for the CAS(ME)\({}^{2}\) dataset show that, for the input images of MHI features, the LGNMNet-RF architecture obtained the highest F1-scores of 0.3400 at \(T_{sEXT}=0.100\) s but its scores were still lower than the highest F1-score from LGNMNet without Random Forest classifier of 0.4242 at \(T_{sEXT}=0.400\) s. The reason for this is that the MHI features may not capture the larger facial intensity in MaEs. However, optical flow input features obtained similar results, with Softmax and Random Forest classifier of F1-scores, 0.2249 and 0.2215, but both were still lower than the results of MHI features. Next, the results in Table 4 show the ME and MaE detection experiment for the SAMM-LV dataset.