Autism Spectrum Disorder is a developmental disorder characterized by a deficit in social behaviour and specific interactions such as reduced eye contact and body gestures. Recent advancements in software and hardware multimedia technologies provide the tools for early detecting the presence of this disorder. In this paper we present an approach based on the combined use of machine learning and eye tracking information. More specifically, features are extracted from image content and viewing behaviour, such as the presence of objects and fixations towards the centre of a scene. Those features are used to train a machine learning-based classifier. The obtained results show that the considered features allow to identify children affected by autism spectrum disorder and typically developing ones.

自闭症谱系障碍是一种发展障碍，其特征是社交行为不足和特定的交互作用，例如眼神交流和手势减少。软件和硬件多媒体技术的最新发展为早期发现这种疾病提供了工具。在本文中，我们提出了一种结合使用机器学习和眼动信息的方法。更具体地说，从图像内容和观看行为中提取特征，例如对象的存在和朝向场景中心的注视。这些功能用于训练基于机器学习的分类器。获得的结果表明，所考虑的特征允许识别受自闭症谱系障碍影响的儿童，通常是正在发展中的儿童。