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 gender. 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.

自闭症谱系障碍症（ASD）的人在社交互动和沟通技巧以及受限制的兴趣和重复行为方面显示出障碍，这极大地影响了日常生活。当前对ASD的鉴定涉及一个漫长的过程，需要经验丰富的临床医生来评估多个功能领域。考虑到这一点，我们提出了一种使用视线和人口统计特征描述符（例如受试者的年龄和性别）对多种级别的ASD风险进行分类的方法。我们构建的特征描述符包含了受试者的年龄和性别，以及基于眼睛注视模式的特征。我们还提出了对眼睛凝视模式的分析，以验证所选手制功能的使用。