# A Study on the Application of Walking Posture for Identifying Persons with Gait Recognition
## 3 Results and Discussion  
The total number of experimental participants in this study was 10--the training data for the ten individuals summed up to 3364 sets, each with 60 frames. The test data for the ten individuals added up to 862 sets, each with 60 frames. We took 60 frames of 30 fps video for training, and the data we took were all videos taken at 3.5 m, so the only processing performed on the data was the location normalization. After deep learning, the training accuracy and classification results are shown in Figure 10a,b, respectively. In training and testing ten categories of 10 people, we set the standard point of regularization at the left end of the screen (_xx, yy_). Since this experiment was to convert two categories of the same individual into one category, there are two different scenarios for the two categories: the leftmost and the rightmost. Therefore, we changed the two categories to one with Equation (2). In the category with people walking to the right, we multiplied \(-1\) in the calculation of \(x\) and treated it as the same category as the one walking to the left. \[ \begin{array}{c}X^{\prime}_{ij}=\left(X_{ij}-X_{0}\times(-1)+xx\right)\\ Y^{\prime}_{ij}=\left(Y_{ij}-Y_{0}+yy\right)\end{array}  \tag{2}\]  
After deep learning, the training accuracy and classification results are shown in Figure 11a,b, respectively. Next, we explore the image's frame rate difference in the recognition rate. Since the frame rate of GoPro was set to 60 fps, the jump points 1, 2, 3, 4, and 5 frames are equivalent to 30, 15, 20, 15, and 12 fps. Figure 12a--c show the accuracy of 30 frames, 60 frames, and 90 frames corresponding to each fps, respectively. It can be seen that the best accuracy in the case of 60 frames is 30 fps. Next, we masked the image below the knees. This experiment aimed to determine if the part of the lower leg below the knee could still be practical if the image were occluded. We removed the OpenPose information below the knee, and the points we removed were 11, 14, 19, 20, 21, 22, 23, and 24. After removing these eight points, we used the remaining 17 points for the experiment. We took 60 frames and 30 fps videos for training and testing; the results are shown in Figure 13. We can see that the accuracy rate was about 75%, even though the calf information was gone, so we think this method is feasible for calf masking. Figure 10: Twenty categories of 10 people. (**a**) Training accuracy results; (**b**) classification results. Figure 11: Ten categories of 10 people. (**a**) Training accuracy results; (**b**) classification results. Figure 12: The accuracy of fps for each video. (**a**) Taking 30 frames as a group, the accuracy (\(y\)-axis) of each fps (\(x\)-axis). (**b**) Taking 60 frames as a group, the accuracy (\(y\)-axis) of each fps (\(x\)-axis). (**c**) Taking 90 frames as a group, the accuracy (\(y\)-axis) of each fps (\(x\)-axis). Figure 13: Training results for masking the image below the knees. In comparison with the studies [18; 19; 20], we determined that the image of the figure is sideways and that the two directions can be combined into one direction. Therefore, our method is feasible for both directions. In addition, our method uses an outdoor context and does not require a particular sensor--i.e., only a lens is needed. In addition, we applied LSTM instead of SVM for feature classification. To further explain, in comparison with [19] (Table 1), both scenarios are side images, and our method can combine two directions into one. Therefore, our method works for both directions, not for walking to the right or the left. Although our method had less data, our accuracy rate was 82%, which is higher. Compared to the method [19] (Table 2), which uses the scenario indoors, our method uses the outdoor scenario. In addition, our method is passive. That is, no additional sensors are needed, and the subject does not need to step on the hardware; only a lens is needed, and the people just walk by.