MoodCapture presents a novel approach that assesses depression based on
images automatically captured from the front-facing camera of smartphones as
people go about their daily lives. We collect over 125,000 photos in the wild
from N=177 participants diagnosed with major depressive disorder for 90 days.
Images are captured naturalistically while participants respond to the PHQ-8
depression survey question: \textit{``I have felt down, depressed, or
hopeless''}. Our analysis explores important image attributes, such as angle,
dominant colors, location, objects, and lighting. We show that a random forest
trained with face landmarks can classify samples as depressed or non-depressed
and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides
several insights through an ablation study, feature importance analysis, and
bias assessment. Importantly, we evaluate user concerns about using MoodCapture
to detect depression based on sharing photos, providing critical insights into
privacy concerns that inform the future design of in-the-wild image-based
mental health assessment tools.

MoodCapture 通过智能手机前置相机自动捕捉的图像提出了一种新的评估抑郁症的方法，收集了 177 位被诊断为重度抑郁症的参与者 90 天内超过 125,000 张在日常生活中捕捉到的图像，并通过分析图像属性如角度、主导颜色、位置、物体和照明等，证明了使用面部标记的随机森林可以有效地分类样本为抑郁或非抑郁，并准确预测 PHQ-8 评分，在事后分析中提供了许多洞见，如消融研究、特征重要性分析和偏差评估，重要的是，我们评估了用户对使用 MoodCapture 检测抑郁症的担忧，为未来设计基于图像的在野外心理健康评估工具提供了重要的隐私问题洞察。