# A Study on the Application of Walking Posture for Identifying Persons with Gait Recognition
## 4 Conclusions  
In this study, we proposed a new method of body identification. The accuracy of this method can reach 80%, which means that this method is effective and that the walking posture differs between people. Gender, height, weight, and even the presence or absence of disease will significantly affect the human body's walking posture, and these differences can be used to distinguish between people. If we want to perform gait recognition, in addition to the commonly used methods of RFID and face recognition, we can also use people's walking postures for recognition. This method can also solve the body recognition issue in which only half of the body can be seen. It is also helpful for finding missing persons. It will be suitable if the camera captures the person's side and the face is also covered. At present, the judgment environment is limited. It can be challenging to face various angles in terms of ground, and the distance is limited to about 3.5 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Angle of View** & **People Number** & **Lower-Leg Image Occluded** & **Accuracy** \\ \hline \multirow{2}{*}{Shaik [19]} & Side & \multirow{2}{*}{124} & \multirow{2}{*}{No} & \multirow{2}{*}{65\%} \\  & (one direction) & & & \\ \hline \multirow{2}{*}{Our method} & Side & \multirow{2}{*}{10} & \multirow{2}{*}{Yes} & \multirow{2}{*}{82\%} \\  & (two directions) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of [PERSON] [19] to our method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Scenatio** & **Method** & **Hardware** & **Accuracy** \\ \hline [PERSON] [18] & Indoor & Active & 48 \(\ast\) 48 foot pressure sensors & 94\% \\ \hline Our method & Outdoor & Passive & 1 camera sensor & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of [PERSON] [19] to our method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Image Capture** & **Feature Capture** & **Feature Classifies** & **Accuracy** \\ \hline [PERSON] [20] & MF-GEIs & TCNN & SVM & 68\% \\ \hline Our method & Jumping frames & OpenPose & LSTM & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of [PERSON] [20] to our method.  
In the future, we will use the dual-lens images to extract the joint points and calculate the differences between two images. After the calculation is completed, the data will be projected to a distance of 3.5 m in proportion. To improve accuracy, we expect to change the parameters of the training alliance, which is also our future research aim. Moreover, there are several possibilities for expansion in the future. For example, step recognition could be used to preliminarily determine whether a person is the owner of a certain car or the owner of a certain house. Alternatively, we could add step recognition from different perspectives.  
Conceptualization, Y.-S.T. and S.-J.C.; methodology, Y.-S.T.; software, S.-J.C.; validation, S.-J.C.; formal analysis, S.-J.C.; writing--original draft preparation, S.-J.C.; writing--review and editing, Y.-S.T.; visualization, S.-J.C.; supervision, Y.-S.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Ministry of Science and Technology (contracts MOST-110-2634-F-A49-006 and 110-2634-F-009-018).  
Not applicable.  
The authors declare no conflict of interest.