Social visual behavior, as a type of non-verbal communication, plays a central role in studying social cognitive processes in interactive and complex settings of autism therapy interventions. However, for social visual behavior analytics in children with autism, it is challenging to collect gaze data manually and evaluate them because it costs a lot of time and effort for human coders. In this paper, we introduce a social visual behavior analytics approach by quantifying the mutual gaze performance of children receiving play-based autism interventions using an automated mutual gaze detection framework. Our analysis is based on a video dataset that captures and records social interactions between children with autism and their therapy trainers (N=28 observations, 84 video clips, 21 Hrs duration). The effectiveness of our framework was evaluated by comparing the mutual gaze ratio derived from the mutual gaze detection framework with the human-coded ratio values. We analyzed the mutual gaze frequency and duration across different therapy settings, activities, and sessions. We created mutual gaze-related measures for social visual behavior score prediction using multiple machine learning-based regression models. The results show that our method provides mutual gaze measures that reliably represent (or even replace) the human coders' hand-coded social gaze measures and effectively evaluates and predicts ASD children's social visual performance during the intervention. Our findings have implications for social interaction analysis in small-group behavior assessments in numerous co-located settings in (special) education and in the workplace.

翻译：社会视觉行为作为一种非语言沟通方式，在自闭症疗愈干预的互动且复杂环境中研究社会认知过程中扮演核心角色。然而，针对自闭症儿童的社会视觉行为分析，人工收集注视数据并对其进行评估颇具挑战性，因为这对人工编码者而言耗费大量时间和精力。在本文中，我们提出一种社会视觉行为分析方法，通过使用自动化相互注视检测框架量化接受游戏式自闭症干预的儿童的相互注视表现。我们的分析基于一个视频数据集，该数据集捕捉并记录了自闭症儿童与其治疗师之间的社会互动（N=28次观察，84个视频片段，总时长21小时）。通过将自动化相互注视检测框架得出的相互注视比率与人工编码的比率值进行比较，评估了该框架的有效性。我们分析了不同疗愈环境、活动及疗程中的相互注视频率与持续时间。利用多种基于机器学习的回归模型，我们构建了与社会视觉行为得分预测相关的相互注视度量指标。结果表明，我们的方法能够提供可可靠替代（甚至取代）人工编码者手工标注的社会注视度量指标，并有效评估及预测自闭症谱系障碍儿童在干预过程中的社会视觉表现。我们的发现对（特殊）教育及职场等多人共处场景中小组行为评估的社会互动分析具有启示意义。