Abstract

People with autism spectrum disorder (ASD) display impairments in social interaction and communication skills, as well as restricted interests and repetitive behaviors, which greatly affect daily life functioning. Current identification of ASD involves a lengthy process that requires an experienced clinician to assess multiple domains of functioning. Considering this, we propose a method for classifying multiple levels of risk of ASD using eye gaze and demographic feature descriptors such as a subject’s age and gennder. We construct feature descriptors that incorporate the subject’s age and gender, as well as features based on eye gaze patterns. We also present an analysis of eye gaze patterns validating the use of the selected hand-crafted features. We assess the efficacy of our descriptors to classify ASD on a National Database for Autism Research dataset, using multiple classifiers including a random forest, C4.5 decision tree, PART, and a deep feedforward neural network.

Papers

1. D. Fabiano, S. Canavan, H. Agazzi, S. Hinduja, and D. Goldgof. Gaze-based Classification of Autism Spectrum Disorder, Pattern Recognition Letters, 135, pp. 204-212, 2020.
2. S. Canavan, M. Chen, S. Chen, R. Valdez, M. Yaeger, H. Lin, and L. Yin. Combining Gaze and Demographic Feature Descriptors for Autism Classification, ICIP, 2017.

Other Related Papers

1. Sk R. Jannat and S. Canavan. Classification of Autism Spectrum Disorder Across Age using Questionnaire and Demographic Information, ICPRW, 2021.
2. Sk R. Jannat, H. Agazzi, and S. Canavan. Context-based Dataset for Analysis of Videos of Autistic Children, FG, 2024.