Depression and anxiety detection method based on serialized facial expression imitation

Facial recognition techniques are widely employed for automatic detection of depression and anxiety. However, current studies overlook the impact of varying spatial resolutions on model performance and lack a mechanism to share attention regions across sequential data. To advance research in this area, we conducted the Voluntary Facial Expression Mimicry Experiment (VFEM) and constructed the VFEM dataset. We also introduce the SFE-Former, a sequential facial expression recognition model designed for detecting depression and anxiety. SFE-Former features a mechanism that shares attention regions across sequence data, allowing each data point to enhance its features by leveraging shared information. Additionally, the model integrates features from different scales using fusion and weighting strategies. The experimental results indicate that SFE-Former achieved impressive accuracy rate: 0.893 for depression detection, 0.889 for anxiety detection, and 0.780 for co-occurrence detection of depression and anxiety. Meanwhile, SFE-Former also obtained state-of-theart (SOAT) results on AVEC2014 dataset. This work can enhance the accuracy of identifying patients with depression and anxiety, providing doctors with reliable auxiliary diagnosis. The source code for SFE-Former is accessible at https://github.com/lulin-6k/SFE-Former.