# Comparing Approaches for Explaining DNN-Based Facial Expression Classifications
## 6 Conclusions  
In this work, we have developed an alternative way of explaining a model that classifies facial expressions. In particular, this method displays the most important geometric features (as calculated by SHAP) plotted on the original images. We developed both geometric features-based DNN models and a CNN model. On the small KDEF dataset we used here, the DNN models outperforms the CNN model, although examples can be found in the literature where models have been developed that outperform all of our models. We argue that the more conventional methods for FER are better explainable than the state-of-the-art CNNs, as can be seen in the explanations we developed and compared with methods for visualizing the decision process for CNNs. This might not matter for mundane tasks, but for high-stakes decision processes, it is vital that we can understand what the model is doing. We believe it would be better to focus more on developing and using interpretable models or models that are better to explain in critical areas than the prevalent black-box models. The geometric feature explanations in this study are based on a DNN, which admittedly is a black-box model as well. However, the explanations we have developed here are not limited to such a model and can be generalized to other types; the explanations are model-agnostic given the geometric feature set. In other words, any model that can make use of geometric features could use the explanations. An intrinsically interpretable logistic regression model could also visualize its decisions in the same way. More recently developed interpretable models that can perform more on-par with deep-learning models such as [21] can also use this technique to visualize their decisions. This study can be extended by using a more extensive dataset with less posed images than the KDEF dataset and seeing how the geometric features perform in such an environment. Furthermore, a dataset with more emotions than these seven could be tested. Furthermore, the deployed CNN can benefit from a multi-stage fine tuning as in [15]. Lastly, the user study especially can be extended in quite a few ways and was limited in this work. Further research should be put in the human evaluation of the proposed method. In conclusion, we have explored and laid the groundwork for a different way of explaining a system for FER. The first results regarding the quality and plausibility of these explanations look promising. Questions on several points still exist and are open for further examination. K.t.B. : Conceptualization, methodology, scripting, experimental validation, writing--original draft preparation, visualization; H.K. : conceptualization, methodology, analysis of results, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. At the beginning of the user study conducted to evaluate the DNN/CNN explanations, the participants digitally provided an informed consent for their evaluations to be included in the study. See Appendix B.2 for details. KDEF dataset is available for research studies from the data owners: [[https://www.kdef.se/](https://www.kdef.se/)]([https://www.kdef.se/](https://www.kdef.se/)) (accessed on 1 December 2021). Scripts to reproduce this work are available at [[https://github.com/kayatb/GeomExp](https://github.com/kayatb/GeomExp)]([https://github.com/kayatb/GeomExp](https://github.com/kayatb/GeomExp)) (accessed on 1 October 2022). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: