IEEE Trans Cybern. 2022 Jul;52(7):6504-6517. doi: 10.1109/TCYB.2022.3165063. Epub 2022 Jul 4.
Biomarkers, such as magnetic resonance imaging (MRI) and electroencephalogram have been used to help diagnose autism spectrum disorder (ASD). However, the diagnosis needs the assist of specialized medical equipment in the hospital or laboratory. To diagnose ASD in a more effective and convenient way, in this article, we propose an appearance-based gaze estimation algorithm-AttentionGazeNet, to accurately estimate the subject's 3-D gaze from a raw video. The experimental results show its competitive performance on the MPIIGaze dataset and the improvement of 14.7% for static head pose and 46.7% for moving head pose on the EYEDIAP dataset compared with the state-of-the-art gaze estimation algorithms. After projecting the obtained gaze vector onto the screen coordinate, we apply accumulated histogram to taking into account both spatial and temporal information of estimated gaze-point and head-pose sequences. Finally, classification is conducted on our self-collected autistic children video dataset (ACVD), which contains 405 videos from 135 different ASD children, 135 typically developing (TD) children in a primary school, and 135 TD children in a kindergarten. The classification results on ACVD shows the effectiveness and efficiency of our proposed method, with the accuracy 94.8%, the sensitivity 91.1% and the specificity 96.7% for ASD.
生物标志物，如磁共振成像（MRI）和脑电图，已被用于帮助诊断自闭症谱系障碍（ASD）。然而，诊断需要在医院或实验室使用专门的医疗设备。为了更有效地、更方便地诊断 ASD，在本文中，我们提出了一种基于外观的注视估计算法——AttentionGazeNet，可从原始视频中准确估计对象的 3D 注视。实验结果表明，在 MPIIGaze 数据集上，与最先进的注视估计算法相比，我们的算法具有竞争力；在 EYEDIAP 数据集上，对于静态头部姿势的提高了 14.7%，对于动态头部姿势的提高了 46.7%。在将获得的注视向量投影到屏幕坐标上后，我们应用累积直方图来考虑估计的注视点和头部姿势序列的空间和时间信息。最后，我们对自己收集的自闭症儿童视频数据集（ACVD）进行分类，该数据集包含来自 135 个不同 ASD 儿童、135 个小学正常发育（TD）儿童和 135 个幼儿园正常发育（TD）儿童的 405 个视频。ACVD 的分类结果表明了我们提出的方法的有效性和效率，对于 ASD 的准确率为 94.8%，灵敏度为 91.1%，特异性为 96.7%。
