# Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production
###### Abstract  
The computational analysis of facial expressions is an emerging research topic that could overcome the limitations of human perception and get quick and objective outcomes in the assessment of neurodevelopmental disorders (e.g., Autism Spectrum Disorders, ASD). Unfortunately, there have been only a few attempts to quantify facial expression production and most of the scientific literature aims at the easier task of recognizing if either a facial expression is present or not. Some attempts to face this challenging task exist but they do not provide a comprehensive study based on the comparison between human and automatic outcomes in quantifying children's ability to produce basic emotions. Furthermore, these works do not exploit the latest solutions in computer vision and machine learning. Finally, they generally focus only on a homogeneous (in terms of cognitive capabilities) group of individuals. To fill this gap, in this paper some advanced computer vision and machine learning strategies are integrated into a framework aimed to computationally analyze how both ASD and typically developing children produce facial expressions. The framework locates and tracks a number of landmarks (virtual electromyography sensors) with the aim of monitoring facial muscle movements involved in facial expression production. The output of these virtual sensors is then fused to model the individual ability to produce facial expressions. Gathered computational outcomes have been correlated with the evaluation provided by psychologists and evidence has been given that shows how the proposed framework could be effectively exploited to deeply analyze the emotional competence of ASD children to produce facial expressions. assistive technology; autism; facial expressions; computer vision +
Footnote †: journal: Medical  
tools. On the one hand, high costs make their pervasive use economically unsustainable for most private and public organizations and, on the other hand, their effectiveness in the assessment of neurodevelopmental disorders is controversial. This is even truer in the case of Autism Spectrum Disorders (ASD) [3], particularly in the case of treatment of children. Accurate evaluation, using non-invasive tools, is becoming a primary need, also considering the increase of ASD prevalence (1/59) [4] in the general population. In particular, since it is well known that ASD children show a clear deficit in the quality of facial expression production compared to typically developing ones [5; 6], some computer vision and machine learning techniques can be successfully implemented to automatically assess emotional skills in a not invasive and accurate way [7] and, finally, to give an automatic assessment of autism spectrum disorders. Unfortunately, to the best of our knowledge, there are no works exploiting the aforementioned computational technologies to provide a comprehensive study carried out comparing psychologists and automatic outcomes to quantify children's ability to produce basic emotions. Prior works mainly report qualitative assessment (ASD vs. non-ASD behavioral features) or just rough quantitative assessments (e.g., smiling/not smiling). A pioneering approach towards this challenging research line was the one in [8]: it introduced an algorithmic pipeline able to analyze facial dynamics using a continuously updated and personalized reference model, differently from the plethora of approaches in the literature that lies on predefined facial models. Unfortunately, that work had two main drawbacks: its outcomes were validated only on ASD children and, besides, it did not exploit the great potential of convolutional neural networks. This leads to an incomplete analysis of its potential in assessing ASD, to a sub-optimal correlation between numerical outcomes and the strength of facial expressions and, last but not least, to collect unreliable outcomes especially in the cases of non-frontal head pose and non-collaborative behaviors of children.