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The ability to interpret social cues comes naturally for most people, but forthose living with Autism Spectrum Disorder (ASD), some experience a deficiencyin this area. This paper presents the development of a multimodal augmentedreality (AR) system which combines the use of computer vision and deepconvolutional neural networks (CNN) in order to assist individuals with thedetection and interpretation of facial expressions in social settings. Theproposed system, which we call AEGIS (Augmented-reality Expression GuidedInterpretation System), is an assistive technology deployable on a variety ofuser devices including tablets, smartphones, video conference systems, orsmartglasses, showcasing its extreme flexibility and wide range of use cases,to allow integration into daily life with ease. Given a streaming video camerasource, each real-world frame is passed into AEGIS, processed for facialbounding boxes, and then fed into our novel deep convolutional time windowedneural network (TimeConvNet). We leverage both spatial and temporal informationin order to provide an accurate expression prediction, which is then convertedinto its corresponding visualization and drawn on top of the original videoframe. The system runs in real-time, requires minimal set up and is simple touse. With the use of AEGIS, we can assist individuals living with ASD to learnto better identify expressions and thus improve their social experiences.

AEGIS: A real-time multimodal augmented reality computer vision based  system to assist facial expression recognition for individuals with autism  spectrum disorder