# Lightweight Micro-Expression Recognition on Composite Database
## 5 Conclusions and Future Work  
MER has huge potential applications in various domains but is challenging due to the intricate and low-intensity facial movements as well as the small datasets available. In this work, a novel lightweight deep learning model, Efficient-ME, with just 867k parameters, is proposed by truncating the baseline EfficientNet-B0 model after the 5 th MBConv blocks pre-trained on a macro-expression database, FER2013, before fine-tuned and evaluated on the composite dataset. This work has demonstrated that a lightweight deep learning model is able to achieve superior MER performance with the consideration that the model must still be able to extract high-level features. Future comparative study of the lightweight limit of the deep learning model will reveal the deep learning network constraints for MER. It was also confirmed that utilizing apex frames as input with no other features' pre-extraction steps \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **UF1 Score** & **UAR Score** & **F1-Neg** & **F1-Pos** & **F1-Surp** \\ \hline
**Efficient-ME with FL** & **0.98** & **1.178** & **0.99** & **0.98** & **0.98** \\ Efficient-ME with CE & 0.916 & 1.109 & 0.94 & 0.93 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Loss function evaluation for Efficient-ME pre-trained on balanced FER2013. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **UF1 Score** & **UAR Score** & **F1-Neg** & **F1-Pos** & **F1-Surp** \\ \hline Efficient-ME with FL & 0.97 & 1.165 & 0.98 & 0.97 & 0.97 \\
**Efficient-ME with CE** & **0.99** & **1.191** & **0.99** & **0.99** & **0.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Loss function evaluation for Efficient-ME pre-trained on imbalanced FER2013. is sufficient for capturing details pertaining to micro-expressions. The combination of the lightweight model with low input complexity utilizing a static apex frame image is proven to achieve state-of-the-art performance compared to other shallow and lightweight MER models. Furthermore, in the transfer learning's source databases experiment, FER2013 is revealed to be more effective than by using a massive general image database such as ImageNet or lab-controlled and smaller facial expression dataset such as CK+. This proves the importance of selecting a source database that is closely related to the target task as well as diverse enough from which to learn general features. Future work that evaluates the different macro-expression databases using the same baseline model and evaluation protocol will add understanding of the influence of source data of transfer learning for MER. In dealing with an imbalanced dataset problem, it can be established that using weighted multi-class focal loss can yield high MER performance when utilizing transfer learning if the source database's class distribution does not match the target, rendering complex data augmentation strategies redundant. In the future, the proposed MER framework can be extended for evaluating 5-class MER. This will necessitate a standard emotion class labelling across the different databases and increase the available micro-expression data. Conceptualization, N.A.A.R. ; methodology, N.A.A.R. ; software, N.A.A.R. ; validation, N.A.A.R. ; formal analysis, N.A.A.R. ; investigation, N.A.A.R. ; resources, N.A.A.R. ; data curation, N.A.A.R. ; writing--original draft preparation, N.A.A.R. ; writing--review and editing, N.A.A.R. and S.S.; visualization, N.A.A.R. ; supervision, S.S.; project administration, N.A.A.R. ; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Universiti Kebangsaan Malaysia, grant number GP2020, GP2021, FTM1 and FTM3. Ethical review and approval were waived for this study due to the use of public databases, which were conducted according to the guidelines of the Declaration of Helsinki and approved by the relevant review boards. We complied with the terms of use of the databases regarding the publication of data.