# Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production
## 6 Comparison with Leading Approaches and Technical Discussion
It follows that outcomes of classical approaches (blue and orange lines) are not suitable to quantify the abilities in facial expression production since both approaches showed no significant correlation the strength of the facial expression production and the automatically gathered scores. On the other hand, the approach in [8] (grey line) showed an appreciable level of correlation and the proposed approach a desirable very high level of correlation. In the second comparison step, the aforementioned approaches were evaluated in terms of accuracy with respect to manual annotations provided by the team of psychologists (on both ASD and TD videos). Table 10 reports the accuracy values related to each comparing method in terms of matching between gathered scores and manual annotations. At a first glance, from the table, It is evident that CNN based approaches, i.e., the ones in [56, 57], perform worse than approaches based on statistical modeling of non-emotional face configurations like the one proposed in this paper and the one introduced in [8]. In fact, besides to provide output scores strictly correlated to the ability in producing a required expression (as the previous comparison proved), the statistical modeling of non-emotional face configurations makes the related frameworks able to adapt their internal parameters to the individual behaviors. Through this desirable feature they can then embed stereotyped movements and to highlight even subtle voluntary movements of facial muscles with respect to the inexpressive facial model, continuously updated, that the system uses as a reference baseline. However, Table 10 shows also that the improved pipeline proposed in this paper is able to better understand actual emotional dynamics of the face by exploiting the capability of introduced convolutional approaches for accurately detect face and to precisely positioning facial landmarks even under severe occlusions and extreme poses. Certainly not all critical situations have been resolved (as reported in Figure 6), but the comparisons make is possible to state that a step \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **f1** & **P** & **R** & **TP** & **FP** & **FN** & **TN** \\ \hline
[56] & 0.80 & 0.79 & 0.81 & 79\% & 17\% & 21\% & 83\% \\
[57] & 0.81 & 0.82 & 0.80 & 80\% & 15\% & 20\% & 85\% \\
[8] & 0.82 & 0.83 & 0.82 & 82\% & 14\% & 18\% & 86\% \\ Proposed & 0.86 & 0.88 & 0.85 & 84\% & 10\% & 16\% & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quantitative comparison with some state-of-the-art approaches. Figure 17: Comparison of the scores computed by different approaches on two subsets of videos containing strongly and hinted execution of facial expressions. forward has been made towards the alignment between the human annotations and the automatic scores provided by an automatic system. In light of the encouraging experimental results, it is useful to discuss how each component in the proposed pipeline affects the overall performance. The most practical way to derive some useful considerations about this important issue is to deeply analyze the results of the above experimental comparisons. From the first comparison step, it emerged that the face analysis based on a personalized, continuously updated, model of the non-expressive face is a fundamental step to make possible a convincing quantification of the ability in performing facial expression. Besides, given that detecting the face is not a particularly complex task in this context (very few faces, not a cluttered scene, zoomed images), from the second comparing step, it is possible to derive that a key role is played by the landmark detection and tracking step. This is particularly evident by observing outcomes of the proposed approach with respect to the one in [8] in which the same strategy was used to perform the subsequent face analysis. Facial landmark detection algorithms can be categorized into three major categories [58]: holistic methods, Constrained Local Model (CLM) methods, and regression-based methods.