# Attention-Guided Network Model for Image-Based Emotion Recognition
## 6 Conclusions  
In this article, the advantages of using attention-integrated modules for facial emotion recognition are highlighted and a new attention module with a guided training pipeline is proposed. The proposed attention-guided facial emotion recognition (AGFER) module couples spatial and channel importance and works with the guidance strategy to emphasize key descriptors in the images for a more robust outcome. The attention-infused models were able to outperform the base model with a margin greater than 60% in terms of mean accuracy given the training settings applied. The AGFER model achieved comparable performance to the squeeze and excitation (SE) and convolutional block attention module (CBAM)-integrated models with the OULU-CASIA validation set while outperforming them with a mean margin of 3.60% with the FACES dataset. The proposed attention-guidance strategy showed the capabilities of attention-laced networks in improving performance by focusing on areas of relative importance towards emotion classification and reducing the time allotted for training, thereby saving resources.  
Conceptualization, H.A. and K.M.; methodology, H.A.; software, H.A.; validation, H.A.; formal analysis, H.A., J.G.C. and K.M.; investigation, H.A.; resources, J.G.C. and K.M.; data curation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, A.B., J.G.C. and K.M.; visualization, H.A.; supervision, J.G.C. and K.M.; project administration, K.M.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.  
This research is partially funded by the German Federal Ministry of Research and Education (BMBF) under grant CoHMed/PersonaMed-A 13 FH5I06 IA and LESSON FKZ: 3 FH5E10 IA, as well as grants from KOMPASS funded by the Ministerium fur Wissenschaft, Forschung und Kunst (MWK) of Baden-Wuerttemberg Germany, ERAPERMED2022-276--ETAP BMG FKZ 2523 FSB110, and the Deutscher Akademischer Austauschdienst (DAAD) under grant AIDE-ASD FKZ 57656657.  
Not applicable.  
The databases used in this study were the (OULU-CASIA NIR and VIS facial expression database) and the (FACES database). The OULU-CASIA NIR and VIS facial expression database dataset ([[https://www.oulu.fi/en/university/faculties-and-units/faculty-information-technology-and-electrical-engineering/center-machine-vision-and-signal-analysis](https://www.oulu.fi/en/university/faculties-and-units/faculty-information-technology-and-electrical-engineering/center-machine-vision-and-signal-analysis)]([https://www.oulu.fi/en/university/faculties-and-units/faculty-information-technology-and-electrical-engineering/center-machine-vision-and-signal-analysis](https://www.oulu.fi/en/university/faculties-and-units/faculty-information-technology-and-electrical-engineering/center-machine-vision-and-signal-analysis)) (accessed on 6 October 2020)) and the FACES database ([[https://faces.mpdl.mpg.de/imeji/](https://faces.mpdl.mpg.de/imeji/)]([https://faces.mpdl.mpg.de/imeji/](https://faces.mpdl.mpg.de/imeji/)) (accessed on 27 August 2021)) are available from the respected publishers upon request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Overall Stride** & **AGFER** & **SEFER** & **CBAMFER** \\ \hline \multirow{2}{*}{OULU-CASIA} & 32 & 86.81\% \(\pm\) 0.64 & 86.00\% \(\pm\) 0.74 & 86.84\% \(\pm\) 1.69 \\  & 16 & **89.19\% \(\pm\) 0.75** & **88.73\% \(\pm\) 0.91** & **89.77\% \(\pm\) 0.70** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean classification accuracy over all segments and emotion classes for each model at both overall network architecture stride dimensions with the OULU-CASIA dataset. Values in bold represent the best performance of the stride dimension.