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Using Computational Models to Understand ASD Facial Expression Recognition Patterns
Irene L. Feng
Dartmouth TR2017-819

Abstract:

Recent advances in computer vision have led to interest in studying how computer vision can simulate our own perception to better understand the intricacies of human neurobiology. Researchers have made strides in computer vision to imitate many facets of human perception, such as object detection, character recognition, and face identification. However, there have been fewer studies that try to model atypical human perception. My thesis focuses specifically on individuals with Autism Spectrum Disorder (ASD) and their deficit in the facial expression recognition (FER) task. I built multiple computer vision models using hand-crafted features and also convolutional neural network architectures to explain the differences of facial expression recognition between typically developing (TD) individuals and individuals with ASD. The models I created that resembled varying levels of configural processing support the hypothesis that diminished configural processing contributes to the FER deficit in individuals with ASD. The models that resembled different areas focus do not support the hypothesis that eye-avoidance and therefore focus on the bottom half of the face contributes to the FER deficit in individuals with ASD.

Note: Senior Honors Thesis. Advisors: Brad Duchaine and Emily Cooper


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   Irene L. Feng, "Using Computational Models to Understand ASD Facial Expression Recognition Patterns." Dartmouth Computer Science Technical Report TR2017-819, May 2017.


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