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Quantifying atypicality in affective facial expressions of children with autism spectrum disorders

We focus on the analysis, quantification and visualization of atypicality in affective facial expressions of children with High Functioning Autism (HFA). We examine facial Motion Capture data from typically developing (TD) children and children with HFA, using various statistical methods, including Functional Data Analysis, in order to quantify atypical expression characteristics and uncover patterns of expression evolution in the two populations. Our results show that children with HFA display higher asynchrony of motion between facial regions, more rough facial and head motion, and a larger range of facial region motion. Overall, subjects with HFA consistently display a wider variability in the expressive facial gestures that they employ. Our analysis demonstrates the utility of computational approaches for understanding behavioral data and brings new insights into the autism domain regarding the atypicality that is often associated with facial expressions of subjects with HFA.