# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 2 Related Works  
Facial expressions and facial movements have a significant impact on nonverbal communication [16; 17; 18; 19]. They reflect an individual's mental and emotional state and are less influenced by personal subjectivity. Conversely, individuals with depression exhibit distinct facial expressions and movements, typically characterized by frowning, drooping eyes, downturned corners of the mouth, and diminished smiles [20; 21]. Analytical modelling based on facial expressions and movements proves to be highly effective in the field of depression recognition.  
In the study of facial features, many scholars utilize the Facial Action Coding System (FACS) as the foundation for constructing features. [PERSON] et al. [22] employed the Active Appearance Model (AAM) to establish 68 facial landmark points, which were subsequently utilized in the classification process. Features such as distance, angle, and area between these landmark points were extracted for analysis. [PERSON] et al. [23] defined facial activity units (AUs) as distinct regions and examined the facial expressions of patients with depression using both original AUs and the defined AU regions. They obtained facial activity patterns based on these analyses. [PERSON] et al. [24] developed an automated FACS system that calculated the frequency of individual AUs and combined AUs in videos. This system was applied to analyze facial expressions in depressed patients.  
[PERSON] et al. [25] utilized a hidden Markov information fusion method to extract the cross-correlation of AU features in a time information sequence, demonstrating its advantages and effectiveness in detecting emotional disorders. [PERSON] et al. [26] chose structural features, including head movement, eyes opening and closing, the average distance between the upper and lower eyelids, and the distance from the mouth to specific landmarks.  
[PERSON] et al. [27] examined facial landmarks as a time series, investigating displacement, velocity, and acceleration as motion-related features, and attained the highest performance indicators in the AVEC2016 competition.  
[PERSON] et al. [17] encoded facial landmark features into grayscale images, generating the Landmark Motion History Image (LMHI) of facial landmarks. White pixels represent the most recent motion, while the darkest grey represents the earliest motion. Grayscale image extraction was performed using Local Binary Patterns (LBPs) and Histogram of Oriented Gradients (HOGs) features. [PERSON] et al. [28] proposed Sampling Sliced Long Short-Term Memory Multiple Instance Learning (SS-LSTM-MIL), a multi-instance learning method that utilizes two-dimensional facial key points as input. This approach employs LSTM and pooling techniques for detecting facial depression in specific video segments. [PERSON] et al. [29] presented the Robust Adversarial Adaptation (SRADDA) method, which addresses domain adaptation across varying scales, label spaces, and depression severity assessments in deep evaluation networks.  
The previous studies demonstrate that these facial features rely on a human clinical understanding of depression. These features are derived by quantifying geometric properties like distance, angle, and area to analyze the facial changes exhibited by individuals with depression. Dynamic information regarding features is represented through the calculation of speed and acceleration for each motion frame. Nevertheless, this method of feature extraction leads to the loss of both the topological structure information of the face and additional features that go beyond clinical understanding.  
This paper presents a framework for depression detection based on visual temporal modality. Facial landmarks are utilized to extract features from patients' faces in both spatial and temporal dimensions. A feature extraction method is then designed to classify depression, addressing the limitation of single-modal depression detection models. The method exhibits strong robustness and low dimensionality.