# Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production
## 4 Participants and Method  
_Participants._ Twenty-seven children were recruited for this study: 17 were children with ASD recruited at two diagnosis and treatment associations in the South of Italy and 10 were typically developing children recruited at a daycare in a little city of South of Italy. For this research Ethical Committee of the Local Health Service gave its approval and informed consent was obtained from children's parents. All children's families were contacted to obtain agreement and they received a cover letter with the project research description and the signed informed consent. _ASD group_. This group is equivalent to the one used in [8]. It included 14 males and 3 females children aged 6-13 years (mean = 8.94; _standard deviation_ = 2.41) and who received a High Functioning--ASD diagnoses by local health service using Autism Diagnostic Observation Schedule (ADOS) scores [51]. Their IQ was assessed by [PERSON]'s Colored Progressive Matrices [52] and the scores were on average level (mean = 105; _standard deviation_ = 10.98; range = 90-120). Furthermore, all children followed a behavioral intervention program using the Applied Behavioral Analysis (ABA). _TD group_. This group included 10 children (6 males) aged 26-35 months of life (Mean = 31.3; _Standard deviation_ = 3.1) who were not referred for any developmental disability. Children were voluntarily recruited aged between the second and third year of life since that age range is the crucial moment in which children start developing the ability to name and recognize facial expression (e.g., [9; 10; 11]) of basic emotions (happiness, sadness, fear, and anger). Table 3 describes the sample divided by children's group. Here it could be useful to make a clarification: the main aim of the paper is to quantify facial expression production while it is not completely developed. For this reason, the experimental setup involved 2-3 years old TD children. Evaluation of TD children of the same age of ASD ones would \begin{table}
\begin{tabular}{c c} \hline
**Expression** & **Measures** \\ \hline H & \(M_{uf}^{H}=V_{AUI6}\) \\  & \(M_{ff}^{H}=V_{AUI2}\) \\ \hline S & \(M_{uf}^{S}=max(V_{AUI1},V_{AUI4})\) \\  & \(M_{if}^{S}=V_{AUI5}\) \\ \hline F & \(M_{uf}^{F}=min(max(V_{AUI2},V_{AUI2}),V_{AUI4},V_{AUS})\) \\  & \(M_{ff}^{F}=max(V_{AUI20},V_{AUI26})\) \\ \hline A & \(M_{uf}^{A}=max(V_{AUI4},V_{AUS},V_{AUI7})\) \\  & \(M_{ff}^{A}=max(max(V_{AUI9},V_{AUI22}),min(V_{AUI7},V_{AUI25}))\) \\ \hline \end{tabular}
\end{table} 
Table 2: Computational measures of production ability for H = Happiness; S = Sadness; F = Fear; A = Anger. lower and upper facial part are indicated by \(uf\) and \(lf\) subscripts respectively. Time index has been omitted for better table readability. \begin{table}
\begin{tabular}{c c c} \hline
**Variables** & **ASD Group** & **TD Group** \\ \hline Children’s gender & M = 13; F = 4 & M = 6; F = 4 \\ Average children’s age & 8.9 years (2.47) & 31.35 (3.11) \\ Children’s birth order & First born = 10 & First born = 6 \\  & Second-born and more = 5 & Second-born and more = 4 \\ Mother’s mean age \({}^{*}\) & 43.3 (4.6) & 36.9 (4.95) \\ Father’s mean age \({}^{*}\) & 47.3 (3.9) & 41 (7.45) \\ Level of maternal education & High = 15; Low = 2 & High = 9; Low = 1 \\ Level of paternal education & High = 14; Low = 3 & High = 7; Low = 3 \\ \hline \end{tabular}
\end{table} 
Table 3: Description of the sample divided by children’s group. Standard deviation is in the bracket. Note that for the level of education “high” means at least 13 years of education whereas “low” indicates 5–8 years of education. bring to pointless results since all the scores would tend to the maximum in subjects having the facial expression production skills fully acquired. What really matters in the considered application context is the level of development of competences, not the chronological age of the involved individuals.