# Key Points' Location in Infrared Images of the Human Body Based on Mscf-ResNet
## 4 Experiment and Analysis
### Experimental Results and Analysis  
To verify the effectiveness of the proposed network model, the proposed network model is compared with other neural network models, including cascaded pyramid network (CPN) [32], convolutional pose machines (CPMs) [19], open pose [28], MobileNetV2 [44], and ResNet-101 [40]. This paper uses the accuracy rate and recall rate to evaluate the effectiveness of the network, for which the formulae are as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100\% \tag{6}\]  
\[recall=\frac{TP}{TP+FN}\times 100\% \tag{7}\]  
Figure 7: The distribution of key points on the human body. where the four parameters are as follows: (i) true positive (_TP_): the prediction is correct, and the positive samples are classified as the number of positive samples; (ii) false positive (_FP_): the prediction is wrong, and the negative samples are classified as the number of positive samples; (iii) true negative (_TN_): the prediction is correct, and the negative samples are classified as negative samples; and (iv) false negative (_FN_): the prediction is wrong, and the positive samples are classified as negative samples. The human infrared image data set is used to train and test each neural network. The accuracy of each key point and the average accuracy of all points are shown in Table 1. It can be seen from Table 1 that both ResNet-101 and the proposed network model have an accuracy rate of more than 90%, but it obviously shows that the proposed network model performs better, with an average accuracy rate of 93.6%. As shown in Figure 8, each neural network is used to locate the key points of the infrared image of the human body in different poses. When the size and posture of the human body change, the proposed network model can locate the key points more accurately. Therefore, the proposed network model has better key point location performance than other neural network models. The training process of different networks, shown in Figure 9, demonstrates the accuracy of different epochs. The accuracy of CPM was the lowest; the accuracy of open pose was improved, but open pose could not extract the information of multi-scale context features of the image, so the positioning performance was not outstanding. Compared with MobileNetV2, ResNet-101 had a deeper network structure, so it had a higher accuracy. CPN used ResNet as the backbone to extract richer features, and the accuracy was only lower than that of the proposed network model; the proposed neural network model used a multi-scale approach. The convolution kernel removed the feature extraction step, so it can extract richer features. Compared with other neural networks, the proposed neural network model obtained the highest accuracy rate and demonstrated very good key point positioning performance. Figure 10 shows the loss values of different neural networks during training. It can be seen from the figure that the proposed neural network model can reduce the loss value to a lower value under a smaller epoch and perform parameter training smoothly. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MobileNetV2** & **ResNet-101** & **CPM** & **Open Pose** & **CPN** & **Ours** \\ \hline Key point L1 (\%) & 88.6 & 89.2 & 82.2 & 84.6 & 91.2 & 93.6 \\ Key point L2 (\%) & 89.5 & 90.3 & 82.8 & 85.3 & 92.6 & 94.2 \\ Key point L3 (\%) & 89.6 & 89.6 & 81.3 & 82.6 & 92.4 & 94.1 \\ Key point L4 (\%) & 88.6 & 89.3 & 81.6 & 83.6 & 91.2 & 93.6 \\ Key point L5 (\%) & 88.9 & 88.6 & 82.4 & 83.4 & 90.6 & 93.5 \\ Key point L6 (\%) & 87.2 & 89.6 & 81.8 & 84.6 & 91.7 & 92.8 \\ Key point R1 (\%) & 88.6 & 90.5 & 81.3 & 85.1 & 91.6 & 93.8 \\ Key point R2 (\%) & 89.4 & 90.6 & 82.6 & 85.2 & 92.1 & 94.6 \\ Key point R3 (\%) & 87.6 & 89.6 & 81.6 & 84.3 & 91.2 & 93.7 \\ Key point R4 (\%) & 87.9 & 88.7 & 82.2 & 83.5 & 90.6 & 92.5 \\ Key point R5 (\%) & 88.6 & 88.9 & 82.4 & 82.6 & 90.8 & 93.4 \\ Key point R6 (\%) & 89.0 & 89.3 & 81.9 & 84.8 & 91.6 & 93.6 \\ Average & 88.6 & 89.5 & 82.0 & 84.1 & 91.5 & 93.6 \\ \hline \end{tabular}
\end{table}