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AbstractAttention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric featuredoi:10.1007/s41666-021-00101-y pmid:35415454 pmcid:PMC8982782 fatcat:pxlwf2d36vhttflqjhy73fbl2u