Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD.