用于心理健康评估的面部行为动力学建模
Modeling Dynamics of Facial Behavior for Mental Health Assessment
摘要：
面部动作单位强度是面部行为分析的常用描述符。然而，当一次只有少数几个被激活时，FAU是稀疏表示的。在这项研究中，我们探索的可能性，表示动态的面部表情，采用算法用于词表示在自然语言处理。具体来说，我们在应用全局向量表示（GloVe）算法来学习面部聚类的嵌入之前，对具有5.3M帧的时间面部表情的大型数据集进行聚类。我们评估了我们的学习表征的有用性在两个下游任务：精神分裂症症状估计和抑郁症严重程度回归。这些实验结果表明，我们的方法的潜在有效性，以改善评估心理健康症状的基线模型，单独使用FAU强度。
英文摘要：
Facial action unit (FAU) intensities are popular descriptors for the analysis of facial behavior. However, FAUs are sparsely represented when only a few are activated at a time. In this study, we explore the possibility of representing the dynamics of facial expressions by adopting algorithms used for word representation in natural language processing. Specifically, we perform clustering on a large dataset of temporal facial expressions with 5.3M frames before applying the Global Vector representation (GloVe) algorithm to learn the embeddings of the facial clusters. We evaluate the usefulness of our learned representations on two downstream tasks: schizophrenia symptom estimation and depression severity regression. These experimental results show the potential effectiveness of our approach for improving the assessment of mental health symptoms over baseline models that use FAU intensities alone.
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