Distinction of stress and non-stress tasks using facial action units

Long-exposure to stress is known to lead to physical and mental health problems. But how can we as individuals track and monitor our stress? Wearables which measure heart variability have been studied to detect stress. Such devices, however, need to be worn all day long and can be expensive. As an alternative, we propose the use of frontal face videos to distinguish between stressful and non-stressful activities. Affordable personal tracking of stress levels could be obtained by analyzing the video stream of inbuilt cameras in laptops. In this work, we present a preliminary analysis of 114 one-hour long videos. During the video, the subjects perform a typing exercise before and after being exposed to a stressor. We performed a binary classification using Random Forest (RF) to distinguish between stressful and non-stressful activities. As features, facial action units (AUs) extracted from each video frame were used. We obtained an average accuracy of over 97% and 50% for subject dependent and subject independent classification, respectively.

长期处于压力之下会导致身心健康问题。但是我们作为个体如何追踪和监测自身的压力呢？用于测量心率变异性的可穿戴设备已被研究用于检测压力。然而，这类设备需要整天佩戴，而且可能很昂贵。作为一种替代方案，我们提议利用正面人脸视频来区分有压力和无压力的活动。通过分析笔记本电脑内置摄像头的视频流，可以实现对压力水平的经济实惠的个人追踪。在这项工作中，我们对114个时长为一小时的视频进行了初步分析。在视频拍摄过程中，受试者在受到压力源刺激前后进行打字练习。我们使用随机森林（RF）进行二分类，以区分有压力和无压力的活动。作为特征，我们使用了从每个视频帧中提取的面部动作单元（AUs）。对于依赖受试者和不依赖受试者的分类，我们分别获得了超过97%和50%的平均准确率。