# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 4 Experimental Studies  
### GhostNet  
GhostNet [31] is a lightweight neural network with several key features. Firstly, it demonstrates a high efficiency when compared to other neural network structures like ResNet [32] and MobileNetV2 [33]. GhostNet achieves this through faster processing, fewer parameters, and reduced calculations. Secondly, GhostNet utilizes group convolution technology, dividing the convolution kernel into smaller groups. This approach reduces parameter size and calculation requirements for each convolution layer, resulting in an improved operational efficiency. Thirdly, GhostNet incorporates a channel attention mechanism. This involves adding a channel attention module after each convolutional layer to dynamically adjust the weight of each channel. This adjustment enhances the network's learning capability. Lastly, GhostNet introduces a feature reorganization mechanism. This involves rearranging the feature maps generated by different convolutional layers to further enhance training efficiency and network accuracy.  
The Ghost Module (GM) [31] in Figure 8 consists of three steps, namely conventional convolution, Ghost generation, and feature map splicing.  
1. First, by providing the input data \(X\in\mathbb{R}^{c\times h\times w}\), use the convolution operation to obtain the intrinsic feature map \(Y^{\prime}\in\mathbb{R}^{h^{\prime}\times w^{\prime}\times m}\). Where \(c\) is the number of input channels, \(h\) and \(w\) are the height and width of the input data. \[Y^{\prime}=X*f^{\prime}\] (20) where \(f^{\prime}\in\mathbb{R}^{c\times k\times k\times m}\) is the convolution kernel, \(k\times k\) is the size of the convolution kernel, and the bias term is omitted.
2. In order to obtain the required Ghost feature map, each feature map \(y^{\prime}_{i}\) of \(Y^{\prime}\) is used to generate a Ghost feature map with operation \(\Phi_{i,j}\). \[y_{ij}=\Phi_{ij}(y^{\prime}_{j}),\;\;\forall i=1,...,m,j=1,...,s,\] (21)  
Figure 8: The network architecture of GhostNet [31].  
3. The intrinsic feature map obtained in the first step and the Ghost feature map obtained in the second step are spliced (identity connection) to obtain the output of the Ghost module. Inspired by the work presented in [31; 34; 35; 36; 37], we made changes to GhostNet based on the following factors: (1) In our video classification task, the initial input channels vary from traditional images. For images, the initial channel number is 3, representing the RGB channels. However, in this paper, the initial channel number corresponds to the total number of each feature angle designed in Section 3.1. (2) While largely preserving the original GhostNet structure, this section selectively modifies the second part of GhostNet's structure, specifically the GhostNet Bottleneck, with the objective of employing GhostNet as a bottleneck graph to extract the network's backbone. (3) The fourth part of GhostNet, which comprises an FC neural classification network, has been removed and replaced with a 4-class MLP [32] structure for classification purposes. The revised network structure is depicted in Figure 8, and the video processing head is used to shape the unit to fit the GhostNet input. In addition, we have included Table 2, which presents data to facilitate the understanding of the modifications made in GhostNet. The current GhostNet network consists of approximately 2.7 M parameters, and the flops (floating-point operations) are around 1.0 G. The specific parameters in the network structure depicted in Figure 8 are listed in Table 2.  
Table 2 presents the following designations: G-bneck refers to the GhostBottleneck module, #exp represents the expansion distance, #out signifies the number of output channels, and SE indicates the usage of the SE module.