# Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production
## 8 Conclusions  
This paper proposed a novel framework to computationally analyze how both ASD and TD children produce facial expressions. The proposed pipeline was applied to evaluate competence in the production of basic emotions. This competence was evaluated both when it is starting to be acquired in typically developing children and when it is a deficit in ASD children. Numerical outcomes highlighted how the pipeline is accurate (more than existing approaches), quick and objective to evaluate both the strength of facial expressions and how much each facial part is involved in facial expression. The reference baseline consisted of the manual annotations made by a team of psychologists. It is worth noting how this automatic system could have important implications in the treatment of children who are a deficit in emotional competence (e.g., ASD children) since it is able to identify both facial movements which are not detected by human eyes and their strength. Therefore, it could help professionals to understand (a) if child is starting to produce a specific facial expression, (b) which facial part (upper or lower) is starting to be involved in facial expression and thus enhance it, and finally (c) which facial part is not involved by child in facial expression and thus focus intervention on it. All the above considerations allow concluding that the automatic system could be useful for professionals who treat ASD child to obtain a learning trend of acquisition and production of facial expression of basic emotions. A limitation of the present study is the sample size. Future works will also deal with the monitoring of the evolution of children's skills over time to objectively highlight the improvements, for example by comparing the individual ability to produce specific facial expression before and after targeted therapies. Another issue concerns the computation of facial landmarks that, in the proposed approach, were computed without any prior knowledge about the final goal (facial expression recognition) to be accomplished. Several researchers are trying to improve landmark positioning accuracy [59] using different metrics (root mean squared error on ground truth data, some application objective function, landmark detection rate) and different competitions on this topic are hosted in top computer vision conferences [60] revealing excellent performance on the reference datasets. However, there is also a research area that is studying how to specify the landmarks (virtual electromyography sensors) to detect and monitor the facial muscles movements depending on the application context. This is a very interesting perspective and the idea of using size variant patches for landmark detection [61] could help to further improve computational analysis of facial expression production abilities. Learning active landmarks for each AU, i.e., finding the best representative patch size for each landmark in a unified framework is the research line to be pursued. The preliminary experiments have shown that the subtle muscle movements belonging to the upper face require smaller landmark patches while the lower face AUs are detected better in larger patches. How this could impact the outcomes of the proposed pipeline will be investigated in future works. **Author Contributions:** Conceptualization, M.L., C.D. and F.L. ; Data curation, M.L., P.C., P.S., P.L.M. and A.L. ; Formal analysis, M.L. and P.C. ; Investigation, M.L., P.C., A.L. and F.L. ; Methodology, M.L., P.C., S.P. and A.L. ; Project administration, C.D. and F.L. ; Resources, A.C.R. and C.P. ; Software, M.L., P.C., P.S. and P.L.M. ; Supervision, C.D. ; Validation, M.L. and P.C. ; Visualization, M.L., P.C. and P.S. ; Writing--original draft, M.L. ; writing--review and editing, P.C., C.D., P.L.M., A.L., S.P., and F.L. **Funding:** This research received no external funding. **Acknowledgments:** We are grateful to two no-profit associations (Amici di Nico and L'Adelfia) and to day care (Giocando s'impara) where children were recruited.