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Classification of autism severity levels using facial features and eye gaze patterns

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Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with varying degrees of severity. Early diagnosis and classification of autism severity are crucial for personalized intervention and support. This study proposes a novel approach using image processing techniques to analyze facial features and eye gaze patterns to differentiate between mild, moderate, and severe autism cases. A comprehensive dataset comprising facial videos and images of individuals aged 2 to 12 years with ASD was collected, along with normally develo** children. Features such as facial asymmetry is calculated using SIFT feature, eye size, inter-eye distance, and eye openness were extracted for both groups using canny and adaptive thresholding techniques. Support Vector Machine (SVM) classifiers were employed to classify autism cases into three severity levels. Results revealed that eye gaze patterns were significantly lower for autism cases and higher for severe cases. Facial asymmetry was higher for autism cases, showing greater deviations from mild to severe cases. Severe autism cases exhibited extreme stiffness in facial muscle control, leading to the absence of facial expressions. Additionally, inter-eye distance increased, and eye openness decreased for severe autism cases. The proposed method demonstrates promising discrimination performance, as evidenced by high accuracy of 98.70 and sensitivity and specificity of 100 and 97.30. This research contributes valuable insights into the potential use of image processing techniques for early autism diagnosis and effective severity classification.

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  1. Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Thiruchendur, Tamil Nadu, India

    G. Wiselin Jiji

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Jiji, G.W. Classification of autism severity levels using facial features and eye gaze patterns. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19494-0

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