FaceDepth: A Robust Unimodal Depression Detection Framework Using Invariant Facial Landmark Features

Although significant progress has been made in automatic diagnosis systems for depression, most of the work focuses on combining features from multiple modalities to improve classification accuracy, which generates a lot of space-time overhead and feature synchronization problems. This research work proposes a unimodal depression detection framework based on facial expression and facial motion features. Firstly, we propose a robust feature extraction method based on the ratio of facial landmark and theoretically prove that this feature has up-down, left-right translation, depth translation, rotation, and flip invariance. The features extracted based on this method maintain the topological structure relationship of facial landmarks in space and maintain the temporal correlation of frames before and after facial landmarks. Then, we provide a novel idea to solve the classification task of large-unit depression videos. The final depression classification result is obtained by decomposing the depression classification task of large-unit videos into the scoring task of multiple short-sequence units and then through the defined score aggregation function. Our key innovations include: (1) theoretically proven invariant facial landmark ratio features, (2) novel video decomposition into short-sequence units with pseudo-labeling, and (3) efficient SRTSNet architecture. On DAIC-WOZ dataset, our framework achieves F1 = 0.85, outperforming all unimodal methods and matching state-of-the-art multimodal approaches while using only facial features.