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

• URL: http://arxiv.org/abs/2010.11884v1
• Date: Thu, 22 Oct 2020 17:20:38 GMT
• Title: AEGIS: A real-time multimodal augmented reality computer vision based system to assist facial expression recognition for individuals with autism spectrum disorder
• Authors: James Ren Hou Lee, Alexander Wong
• Abstract summary: This paper presents the development of a multimodal augmented reality (AR) system which combines the use of computer vision and deep convolutional neural networks (CNN) The proposed system, which we call AEGIS, is an assistive technology deployable on a variety of user devices including tablets, smartphones, video conference systems, or smartglasses. We leverage both spatial and temporal information in order to provide an accurate expression prediction, which is then converted into its corresponding visualization and drawn on top of the original video frame.
• Score: 93.0013343535411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to interpret social cues comes naturally for most people, but for those living with Autism Spectrum Disorder (ASD), some experience a deficiency in this area. This paper presents the development of a multimodal augmented reality (AR) system which combines the use of computer vision and deep convolutional neural networks (CNN) in order to assist individuals with the detection and interpretation of facial expressions in social settings. The proposed system, which we call AEGIS (Augmented-reality Expression Guided Interpretation System), is an assistive technology deployable on a variety of user devices including tablets, smartphones, video conference systems, or smartglasses, showcasing its extreme flexibility and wide range of use cases, to allow integration into daily life with ease. Given a streaming video camera source, each real-world frame is passed into AEGIS, processed for facial bounding boxes, and then fed into our novel deep convolutional time windowed neural network (TimeConvNet). We leverage both spatial and temporal information in order to provide an accurate expression prediction, which is then converted into its corresponding visualization and drawn on top of the original video frame. The system runs in real-time, requires minimal set up and is simple to use. With the use of AEGIS, we can assist individuals living with ASD to learn to better identify expressions and thus improve their social experiences.

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