Abstract
Accurate facial expression analysis is an essential step in various clinical applications that involve physical and mental health assessments of older adults (e.g. diagnosis of pain or depression). Although remarkable progress has been achieved toward developing robust facial landmark detection methods, state-of-the-art methods still face many challenges when encountering uncontrolled environments, different ranges of facial expressions, and different demographics of the population. A recent study has revealed that the health status of individuals can also affect the performance of facial landmark detection methods on front views of faces. In this work, we investigate this matter in a much greater context using seven facial landmark detection methods. We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face. Our results shed light on limitations of the existing methods and challenges of applying these methods in clinical settings by indicating: 1) a significant difference between the performance of state-of-the-art when tested on the profile or frontal faces of individuals with vs. without dementia; 2) insights on the existing bias for all regions of the face; and 3) the presence of this bias despite re-training/fine-tuning with various configurations of six datasets.
Abstract (translated)
准确的面部表情分析是涉及老年人身心健康评估（如疼痛或抑郁诊断）的各种临床应用中的一个重要步骤。尽管在发展强有力的面部标志性检测方法方面取得了显著进展，但在面对不受控制的环境、不同的面部表情范围和不同的人口统计学时，最先进的方法仍然面临许多挑战。最近的一项研究表明，个人的健康状况也会影响面部标志物检测方法在人脸前视图上的性能。在这项工作中，我们使用七种面部标志物检测方法在更大范围内调查这一问题。我们不仅对正面进行评估，而且对侧面和面部各区域进行评估。我们的研究结果揭示了现有方法的局限性以及在临床环境中应用这些方法的挑战，表明：1）在有痴呆症和无痴呆症个体的侧面或正面测试时，最新技术的表现之间存在显著差异；2）洞察所有区域的现有偏见。脸；以及3）尽管重新培训/微调了六个数据集的各种配置，但仍然存在这种偏差。
URL
https://arxiv.org/abs/1905.07446
