Recent evidence in mental health assessment have demonstrated that facial appearance could be highly indicative of depressive disorder. While previous methods based on the facial analysis promise to advance clinical diagnosis of depressive disorder in a more efficient and objective manner, challenges in visual representation of complex depression pattern prevent widespread practice of automated depression diagnosis. In this paper, we present a deep regression network termed DepressNet to learn a depression representation with visual explanation. Specifically, a deep convolutional neural network equipped with a global average pooling layer is first trained with facial depression data, which allows for identifying salient regions of input image in terms of its severity score based on the generated depression activation map (DAM). We then propose a multi-region DepressNet, with which multiple local deep regression models for different face regions are jointly leaned and their responses are fused to improve the overall recognition performance. We evaluate our method on two benchmark datasets, and the results show that our method significantly boosts state-of-the-art performance of the visual-based depression recognition. Most importantly, the DAM induced by our learned deep model may help reveal the visual depression pattern on faces and understand the insights of automated depression diagnosis.

中文翻译：

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用于从面部图像中识别抑郁症的视觉可解释表示学习

最近心理健康评估的证据表明，面部外观可能是抑郁症的高度指示。虽然以前基于面部分析的方法有望以更有效和客观的方式推进抑郁症的临床诊断，但复杂抑郁症模式的视觉表示方面的挑战阻碍了自动化抑郁症诊断的广泛实践。在本文中，我们提出了一个称为 DepressNet 的深度回归网络，以学习具有视觉解释的抑郁表示。具体来说，配备全局平均池化层的深度卷积神经网络首先使用面部抑郁数据进行训练，这允许根据生成的抑郁激活图 (DAM) 的严重性评分来识别输入图像的显着区域。然后，我们提出了一个多区域 DepressNet，通过它联合学习了针对不同人脸区域的多个局部深度回归模型，并将它们的响应融合在一起，以提高整体识别性能。我们在两个基准数据集上评估了我们的方法，结果表明我们的方法显着提高了基于视觉的抑郁症识别的最新性能。最重要的是，由我们学习的深度模型诱导的 DAM 可能有助于揭示面部的视觉抑郁模式，并了解自动抑郁症诊断的见解。结果表明，我们的方法显着提高了基于视觉的抑郁症识别的最新性能。最重要的是，由我们学习的深度模型诱导的 DAM 可能有助于揭示面部的视觉抑郁模式，并了解自动抑郁症诊断的见解。结果表明，我们的方法显着提高了基于视觉的抑郁症识别的最新性能。最重要的是，由我们学习的深度模型诱导的 DAM 可能有助于揭示面部的视觉抑郁模式，并了解自动抑郁症诊断的见解。