# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 4 Experimental Studies
### Experimental
#### 4.4.2 Experimental Results Comparison
Table 6 presents our method and the results of [PERSON] et al. [50] using the time-space expanded convolutional network (TDCN) and feature attention module (FWA) in different video modalities. Among all the visual modalities. The effectiveness of our method is second only to the approach that utilizes the mixed visual information of facial key points and head pose simultaneously. The F1 values are only 0.1 lower than those achieved by that approach. As shown in Table 6, when using Gaze data alone, they obtained an F1 value of 0.63, whereas our method achieved a 0.17 higher F1 value. For the experiment using Pose data alone, their F1 value was 0.69, which is 0.11 lower than ours. In the case of 2D Landmark data, they obtained an F1 value of 0.72, which is 0.08 lower than our F1 value. When utilizing both 2D Landmark and Gaze data, our method outperformed theirs by 0.07 in F1 values. While using 2D Landmark and Pose simultaneously, our method achieved an F1 value only 0.01 lower than theirs, but the difference in recall rate and precision rate for our method is only 0.06, whereas theirs shows a difference of 0.18. These results indicate that although our method is slightly lower in F1 value, it exhibits a greater robustness and improved clarity and conciseness. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature** & **M** & **P** & **R** & **F1** \\ \hline Gaze & V & 0.85 & 0.50 & 0.63 \\ Pose & V & 0.72 & 0.66 & 0.69 \\
2D landmarks & V & 0.69 & 0.75 & 0.72 \\
2D Landmarks + Pose & V & 0.73 & 0.91 & 0.81 \\
2D Landmarks + Gaze & V & 0.61 & 0.91 & 0.73 \\ Ours Angle & V & 0.77 & 0.83 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the effects of the vision itself. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **M** & **P** & **R** & **F1** \\ \hline [PERSON] et al., 2020 [28] & V & 0.81 & 0.75 & 0.78 \\ [PERSON] et al., 2018 [46] & V & 0.64 & 0.58 & 0.61 \\ [PERSON] et al., 2022 [47] & V + A + T & 0.89 & 0.57 & 0.70 \\ [PERSON] et al., 2018 [48] & V + A + T & 0.71 & 0.83 & 0.77 \\ [PERSON] et al., 2021 [49] & V + A + T & 0.71 & 0.55 & 0.63 \\ [PERSON] & V & 0.77 & 0.83 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the impact of the video modality itself and the data incorporating the video modality. Our comparative analysis reveals that the features we designed exhibit a superior expressive capability. Additionally, this verification highlights the enhanced robustness of our specifically designed angular features compared to general visual unimodal data. To determine the optimal window size for capturing angle features effectively, we performed three sets of ablation experiments, as presented in Table 7. Three indicators are considered for determining the window size. The optimal result is achieved when using a sliding window of 120 s. Therefore, we selected a sliding window size of 120 s for the experiment. In order to assess the model optimization [51; 52; 53], this paper utilizes the ROC scatter plot [54]. As depicted in Figure 9, a total of five models were tested on the DAIC-WOZ [12] dataset: (1) the experiment using the native structure of GhostNet with a sliding window of 3600, referred to as GhostNet; (2) the experiment using the native structure of GhostNet combined with MLP at a sliding window of 3600, referred to as GhostNet+MLP; and (3) the modified GhostNet plus MLP with sliding windows of 5400, 3600, and 1800, referred to as GhostNet + Win1, GhostNet+Best, and GhostNet + Win2, respectively. According to the nature of the ROC scatter diagram, a model located in the upper-left quadrant is considered better, and the larger the area of the circle representing the model, the higher its optimization level. Based on this, the model with a sliding window of 3600, namely GhostNet+Best, performs the best and serves as the final experimental result of this paper. Both the GhostNet + Win2 and GhostNet + Win1 models are inferior to the GhostNet+MLP model due to the sliding window being set too large and too small, respectively.