  Depression is a prevalent mental health disorder that significantly impacts
individuals' lives and well-being. Early detection and intervention are crucial
for effective treatment and management of depression. Recently, there are many
end-to-end deep learning methods leveraging the facial expression features for
automatic depression detection. However, most current methods overlook the
temporal dynamics of facial expressions. Although very recent 3DCNN methods
remedy this gap, they introduce more computational cost due to the selection of
CNN-based backbones and redundant facial features.
  To address the above limitations, by considering the timing correlation of
facial expressions, we propose a novel framework called FacialPulse, which
recognizes depression with high accuracy and speed. By harnessing the
bidirectional nature and proficiently addressing long-term dependencies, the
Facial Motion Modeling Module (FMMM) is designed in FacialPulse to fully
capture temporal features. Since the proposed FMMM has parallel processing
capabilities and has the gate mechanism to mitigate gradient vanishing, this
module can also significantly boost the training speed.
  Besides, to effectively use facial landmarks to replace original images to
decrease information redundancy, a Facial Landmark Calibration Module (FLCM) is
designed to eliminate facial landmark errors to further improve recognition
accuracy. Extensive experiments on the AVEC2014 dataset and MMDA dataset (a
depression dataset) demonstrate the superiority of FacialPulse on recognition
accuracy and speed, with the average MAE (Mean Absolute Error) decreased by 21%
compared to baselines, and the recognition speed increased by 100% compared to
state-of-the-art methods. Codes are released at
https://github.com/volatileee/FacialPulse.
