# Lightweight Micro-Expression Recognition on Composite Database
## 4 Results and Discussions
### Experiment 1: Lightweight Models with Low Input Complexity Can Achieve High MER Performance  
In Table 4, it can be observed that the proposed method, Efficient-ME, achieved significant results with 14% UF1 score improvement over the highest score so far attained by MTM-Net. Although STSTNet is still much smaller than Efficient-ME, their method takes a pre-extracted Optical Flow input that allows their network to be compressed further. Besides, their UFI and UAR scores are average at best. In contrast, the proposed method does not need any prior feature extraction, accomplishing low input complexity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **Input** & **UF1** & **UAR** & **\# Parameters** \\ \hline STSTNet [10] & 2019 & OF & 0.735 & 0.760 & 1.67k \\ ICE-GAN [27] & 2020 & Apex & 0.845 & 0.841 & 21.6 mil \\ MTM-Net [15] & 2020 & Onset-Apex & 0.86 & 0.85 & n/a \\ GEME [16] & 2021 & DI & 0.7221 & 0.7303 & 53 mil \\ RNAS-MER [32] & 2023 & Video & 0.8302 & 0.8511 & 1.91 mil \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Baseline methods on MEGC 2019 composite dataset. OF: Optical Flow. DI: Dynamic images. A smaller variant of the proposed model, Efficient-ME (tr3), with only 68k parameters built by truncating the base EfficientNet-B0 model up to the third block, attained reasonable performance with UF1 score of 0.76, better than the results reported by [16] that use Dynamic Images as input and [10] using pre-extracted optical flow. This demonstrates that complex inputs requiring an involved feature engineering process are not necessary to improve MER as smaller models like Efficient-ME (tr3) can achieve acceptable performance by consuming just apex frame images as input. This also proves that apex frame images contain sufficient and relevant micro-expression information for MER. However, Efficient-ME (tr3) performs worse than the proposed model despite claims by [9, 10, 11] that a smaller and lighter network is better for the MER task. To understand this phenomenon, we examine the output feature maps for both models. Efficient-ME (tr3) generates 40 output features of size 28 \(\times\) 28 at the last MBConv block (Block 3) as can be seen in Figure 5. The features extracted are still extremely low-level consisting of edges' information from the shapes in the image but also from the textures and contours present. This makes the feature map noisier and harder to classify in the dense layer. On the other hand, the proposed model, Efficient-ME, generates 112 features at the last MBConv block (Block 5) with size 14 \(\times\) 14. The output feature map consists of higher-level details aggregated from previous layers like shapes and edges. Figure 6 shows that the model can tune into the general facial movements such as the pulled up upper lip and downturned mouth that indicates the emotion '_Disgust_'. Hence, the output feature map from this deeper network has the advantage of being immune to unnecessary details like the face texture. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **UF1 Score** & **UAR Score** & **\# Parameters** \\ \hline STSTNet [10] & 0.735 & 0.760 & 1.67k \\ ICE-GAN [27] & 0.845 & 0.841 & 21.6 mil \\ MTM-Net [15] & 0.86 & 0.85 & n/a \\ GEME [16] & 0.7221 & 0.7303 & 53 mil \\ RNAS-ME [32] & 0.8302 & 0.8511 & 1.91 mil \\
**Efficient-ME** & **0.987** & **1.178** & 867 K \\ Efficient-ME (tr3) & 0.76 & 0.89 & 68 K \\ \hline \hline \end{tabular}
\end{table} 
Table 4: UF1 and UAR scores against baseline methods on composite dataset. Figure 5: Output feature map of Efficient-ME (tr3). From the comparison of the output feature maps of Efficient-ME and Efficient-ME (tr3), it can be inferred that there is a limit to how small a deep learning model can be before it loses its efficacy in features extractions.