# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### System Design
#### 2.3.3 Deep Learning Module  
After preprocessing, the data is passed to the deep learning module, which is processed by the neural network to realize the feature extraction function, and the person detection function is realized through the classifier of the output layer. This section mainly introduces the VGG convolutional neural network [38]. _VGG Convolutional Neural Network_. As deep learning has become an important development direction in machine learning, the use of convolutional neural network (CNN) to solve image classification and recognition problems has gradually become mainstream. As a feedforward neural network, CNN combines an artificial neural network with deep learning, and trains the weights in the network through the improved back propagation algorithm based on gradient to realize the deep learning algorithm. However, VGG, as one of the classical convolutional neural networks, has three convolutional layers, three pooling layers and a fully connected layer. By stacking multiple small-size \(3\times 3\) convolution filters to replace the large convolution kernel, the calculation can be effectively reduced, and the regularization effect and classification accuracy can be improved. The network structure is shown in Figure 9.  
1. Convolutional layer. The convolutional layer is mainly used for gait image feature extraction and recognition. VGG uses a uniform 3 \(\times\) 3 convolutional filter to overlay the input matrix, and then carries out the inner product with the overlaid input submatrix, plus a certain bias value to obtain an element of the output matrix. After sliding with a certain step size, the above steps are repeated to obtain the entire output matrix. The specific calculation method is as follows: \[c_{n}^{i}=\sum_{i\in M}c_{nm}^{i-1}w_{mm}^{i}+b_{n}^{i}\] (10) where \(c_{n}^{i}\) represents the \(n^{th}\) element of the output matrix, \(c_{mn}^{i-1}\) is the element of the \(m^{th}\) row and \(n^{th}\) column of the input matrix, \(w_{mm}^{i}\) is the weight of the \(m^{th}\) row and \(n^{th}\) column, \(b_{n}^{i}\) is the bias value, and \(M\) represents the set of input matrices. 2. Pooling layer. The essence of pooling is sampling, which occurs after convolution, and its process is similar to convolution. The pooling kernel size used by VGG is 2 \(\times\) 2, and the step size is two. The pooling layer selects a certain way to reduce the dimension and compress the input features to eliminate redundant data information, speed up the operation and prevent the algorithm from overfitting. Pooling is similar to the sliding operation of the convolution operation. In the operation of convolution and pooling, the size of the input image and the output image should meet the following relationship:  
\[o=\frac{i+2b-f}{s}+1 \tag{11}\]  
Here, \(i\) represents the size of the input image, \(b\) is the size of the input image boundary fill, \(f\) is the filter size, \(s\) is the sliding step size, and \(o\) is the size of the output image. 3. Fully connected layers. This layer is the same as the traditional neural network, that is, there is a connection relationship between any two meta-neural units of the input layer and the output layer. _Classifier_. The SoftMax classifier is used in the output layer to classify the input data. The SoftMax function has the form:  
\[S(i)=\frac{e^{-\theta_{i}x}}{\sum_{j=1}^{n}e^{-\theta_{j}x}} \tag{12}\]  
Figure 9: VGG Network structure. \(\theta\) is the parameter to be evaluated, \(n\) is the total number of categories, and \(x\) is the input data. Softmax makes \(S(i)\) between [0, 1]. When performing classification, Softmax follows the following principles:  
\[\left(y^{(i)}=n\big{|}x^{(i)};W\right)=\left[ \begin{array}{c}S(y^{(i)}=1\big{|} x^{(i)};W)\\ S(y^{(i)}=2\big{|}x^{(i)};W)\\ \vdots\\ S(y^{(i)}=n\big{|}x^{(i)};W)\end{array} \right]=\frac{1}{\sum_{j=1}^{n}e^{w_{j} x^{(i)}}}\cdot\left[ \begin{array}{c}e^{w_{1}x^{(i)}}\\ e^{w_{2}x^{(i)}}\\ \vdots\\ e^{w_{n}x^{(i)}}\end{array}