  Autism diagnosis presents a major challenge due to the vast heterogeneity of
the condition and the elusive nature of early detection. Atypical gait and
gesture patterns are dominant behavioral characteristics of autism and can
provide crucial insights for diagnosis. Furthermore, these data can be
collected efficiently in a non-intrusive way, facilitating early intervention
to optimize positive outcomes. Existing research mainly focuses on associating
facial and eye-gaze features with autism. However, very few studies have
investigated movement and gesture patterns which can reveal subtle variations
and characteristics that are specific to autism. To address this gap, we
present an analysis of gesture and gait activity in videos to identify children
with autism and quantify the severity of their condition by regressing autism
diagnostic observation schedule scores. Our proposed architecture addresses two
key factors: (1) an effective feature representation to manifest irregular
gesture patterns and (2) a two-stream co-learning framework to enable a
comprehensive understanding of its relation to autism from diverse perspectives
without explicitly using additional data modality. Experimental results
demonstrate the efficacy of utilizing gesture and gait-activity videos for
autism analysis.
