Chen Jingying, Chen Chang, Xu Ruyi, Liu Leyuan
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Computer Science and Artificial Intelligence School, Wuhan University of Technology, Wuhan 430070, China.
Children (Basel). 2024 Oct 28;11(11):1306. doi: 10.3390/children11111306.
Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability.
To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model's interpretability but also provides valuable insights by highlighting the most influential features through attention weights.
Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%.
This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.
面部行为已成为自闭症识别的关键生物标志物。然而，自闭症个体之间的异质性给传统特征提取方法带来了重大障碍，这些方法往往缺乏必要的辨别力。虽然深度学习方法具有潜力，但它们常常因缺乏可解释性而受到批评。
为应对这些挑战，我们开发了一种创新的面部行为特征表征模型，该模型集成了粗粒度和细粒度分析以实现智能自闭症识别。粗粒度分析通过计算与面部行为特征相关的统计量提供整体视图。相比之下，细粒度组件通过采用长短期记忆（LSTM）模型来捕捉头部姿势、面部表情强度和表情类型的时间动态，从而揭示细微 的时间波动。为了充分利用两种分析的优势，我们实现了一种特征级注意力机制。这不仅提高了模型的可解释性，还通过注意力权重突出最具影响力的特征，从而提供有价值的见解。
在自建的自闭症数据集上使用三折交叉验证进行评估时，我们的综合方法实现了88.74%的平均识别准确率，比单独的粗粒度分析高出8.49%。
这一实验结果强调了面部行为特征的改进的通用性，并有效减轻了自闭症患者群体内部明显变异性带来的复杂性，从而有助于更准确和可解释的自闭症识别。
