# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 5 Results and Discussion
### Ablation Study
#### 5.2.2 Machine Learning Classifiers  
A Random Forest classifier is used in the proposed method. However, other classifiers, such as basic fully connected layers with Softmax activation layers, Support Vector Classifier (SVC) [30], Linear Regression [31], and \(k\)-Nearest Neighbors (\(k\)-NN) [32] machine learning classifiers, have been used to analyse the performance of the proposed network architectures.  
Table 5 illustrates the experimental findings of different machine learning classifiers using the input feature MHI and LGNMNet architecture with a MobileNet V3 backbone  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & \(k\) & **Classifier** & \(T_{sEXT}\) **[s]** & \(T_{fEXT}\) **[frames]** & **TP** & **FP** & **FN** & **F1** \\ \hline \multirow{2}{*}{OF} & Softmax & 0.000 & 0 & 76 & 221 & 83 & 0.3333 \\  & Random Forest & 0.000 & 0 & 68 & 127 & 91 & 0.3842 \\ \hline \multirow{6}{*}{MHI} & & 0.000 & 0 & 54 & 250 & 105 & 0.2333 \\  & & 0.035 & 7 & 50 & 141 & 109 & 0.2841 \\  & & 0.065 & 13 & 56 & 212 & 103 & 0.2623 \\  & Softmax & 0.100 & 20 & 45 & 165 & 114 & 0.2439 \\  & & 0.130 & 26 & 51 & 151 & 108 & 0.2825 \\  & 37 & 0.165 & 33 & 49 & 153 & 110 & 0.2715 \\  & & 0.185 & 37 & 54 & 189 & 105 & 0.2687 \\ \cline{2-10}  & & 0.000 & 0 & 68 & 170 & 91 & 0.3426 \\  & & 0.035 & 7 & 59 & 155 & 100 & 0.3164 \\  & & 0.065 & 13 & 63 & 177 & 96 & 0.3158 \\  & Random Forest & 0.100 & 20 & 61 & 157 & 98 & 0.3236 \\  & & 0.130 & 26 & 59 & 126 & 100 & 0.3430 \\  & & 0.165 & 33 & 71 & 164 & 88 & 0.3604 \\  & & 0.185 & 37 & 59 & 191 & 100 & 0.2885 \\ \hline \multirow{2}{*}{OF} & Softmax & 0.000 & 0 & 138 & 99 & 205 & 0.4759 \\  & Random Forest & 0.000 & 0 & 140 & 103 & 203 & 0.4778 \\ \hline \multirow{6}{*}{MHI} & & 0.000 & 0 & 127 & 118 & 216 & 0.4320 \\  & & 0.100 & 20 & 121 & 119 & 222 & 0.4151 \\  & & 0.200 & 40 & 120 & 122 & 223 & 0.4130 \\ \cline{1-1}  & Softmax & 0.300 & 60 & 120 & 128 & 223 & 0.4061 \\ \cline{1-1}  & & 0.400 & 80 & 138 & 128 & 213 & 0.4326 \\ \cline{1-1}  & & 0.500 & 100 & 124 & 117 & 219 & 0.4247 \\ \cline{1-1}  & & 0.600 & 120 & 123 & 120 & 220 & 0.4198 \\ \hline \multirow{6}{*}{MHI} & & 0.000 & 0 & 123 & 126 & 220 & 0.4155 \\  & & 0.100 & 20 & 135 & 146 & 208 & 0.4327 \\ \cline{1-1}  & & 0.200 & 40 & 122 & 127 & 221 & 0.4122 \\ \cline{1-1}  & Random Forest & 0.300 & 60 & 115 & 143 & 228 & 0.3827 \\ \cline{1-1}  & & 0.400 & 80 & 124 & 157 & 219 & 0.3974 \\ \cline{1-1}  & & 0.500 & 100 & 128 & 158 & 215 & 0.4122 \\ \cline{1-1}  & & 0.600 & 120 & 138 & 140 & 205 & 0.4444 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LGNMNet-RF ME F1-scores \(\uparrow\) of SAMM-LV with \(k=37\).  
network. It was found that the Random Forest classifier obtained the highest F1-score of 0.3019, followed by SVC, with an F1-score of 0.2542. The lowest F1-score uses the Softmax activation function as a classifier at 0.1374. The second lowest F1-score was obtained with the Linear Regression machine learning classifier, which shows that machine learning classifiers can improve the detection accuracies compared to conventional fully connected layers with the Softmax activation function.