# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
### Learning Model  
In gesture recognition, one-dimensional CNN identify raw data [32]. This method has high-performance requirements for training algorithms and is rarely used. The extracted data features are two-dimensional images. Meanwhile, the three-dimensional CNN [33] is based on the two-dimensional CNN. First, the training process of the two-dimensional CNN is introduced and then the three-dimensional CNN used in the experiment is introduced [34].  
The core operations of the two-dimensional CNN include two-dimensional convolution operations and two-dimensional pooling operations. In the two-dimensional convolutional operation, the output is the two-dimensional feature map output by the upper layer, and the two-dimensional convolution operation is performed with the convolution kernel of the current layer. The activation function processes the output result, and the two-dimensional pooling operation reduces the dimension. The whole process is shown in Figure 4. Input 8 \(\times\) 8 two-dimensional data map, and perform two-dimensional convolution operation with 3 \(\times\) 3 convolution kernel to obtain 6 \(\times\) 6 intermediate feature map. 3 \(\times\) 3 two-dimensional spatial features are finally obtained through the 2 \(\times\) 2 pooling operation.  
Unlike the two-dimensional CNN input and output are two-dimensional feature maps, the three-dimensional CNN input is a sequence of continuous data frames. Then, three-dimensional convolution operation and three-dimensional pooling operation are used to extract temporal-spatial joint features. The final output contains three-dimensional feature units of a continuous sequence of feature maps \(X(x,y,z)\) in the segment, the team can be expressed as Equation (1) [35].  
\[Y(x,y,z)=X(x,y,z)\otimes H(x,y,z)=\sum_{i=0}^{K_{1}-1}\sum_{j=0}^{K_{2}-1}\sum _{k=0}^{K_{3}-1}X(x+i,y+i,z+k)H(x,y,z)\] (1)  
The symbol \"\(\otimes\)\" represents the convolution operation \(X(x,y,z)\) and \(H(x,y,z)\) denote the three-dimensional feature unit in the previous layer and the three-dimensional convolution kernel unit in the three-dimensional convolution operation. \(K_{1}K_{2},K_{3}\) represent the three-dimensional convolution kernel's length, width, and height information. The whole process is shown in Figure 5. The input is 8 \(\times\) 8 \(\times\) 6 continuous data frames, and after  
Figure 4: The process of the two-dimensional CNN.
+++  
an operation with \(3\times 3\times 3\) convolution units, the \(6\times 6\times 4\) three-dimensional feature units of the middle layer are obtained. Then, after the \(2\times 2\times 2\) three-dimensional pooling operation, the feature unit of \(3\times 3\times 2\) is finally output. Comparing the two-dimensional CNN in Figure 4, it can be found that the production of the two-dimensional CNN is a single frame of two-dimensional data. Therefore, the entire network architecture can only achieve data compression in the spatial dimension and complete spatial feature extraction. The input of the three-dimensional CNN is continuous data frames. Through three-dimensional convolution and three-dimensional pooling, the entire network can achieve compression in both time and space dimensions at the same time and finally complete the time-space joint feature extraction.