Abstract
As a psychological disease, depression needs to be detected and treated. However, the detection of depression is hard, and there is hardly any automated way to assess depression. In this paper, we worked on a dataset named ”FacePsy” which consists of facial landmark data of 25 participants. The depression among the participants was measured using the phq9 scale over a period of time. Using the dataset, we build a transformer-based model with an attention mechanism. The major challenge was to prepare the dataset to fit into the transformer model. The dataset was not evenly balanced, which created a bias in the results. However, we tried to minimize that by adding the self-attention mechanism to our model. In the universal method for evaluation, our proposed method achieved an accuracy of 93%, a Precision of 91%, and an F1-score of 84%. While in the hybrid model evaluation, our proposed method achieved an accuracy of 62% and an F1-score of 46%. This paper was submitted in ”BeyondSmile: Detecting Depression through Facial Behavior and Head Gestures” challenge as part Activity and Behavior Computing 2025 Conference.
https://ror.org/05wv2vq37 
