## 3 Experimental Results
### Performance Comparisons  
The precision rate of the recognized results can be computed by [25]  
\[P\%=\frac{\text{The number of correctly detected people}}{\text{The number of detected people}}\cdot 100\% \tag{4}\]  
In (4), the precision rate \(P\%\) gets a high value when the number of correctly detected people increases. The higher the precision rate value indicates, the more accurate the detection number of people in a photo. The recall rate can reflect the successfully detected number of people. The recall rate can be computed by [25]  
\[R\%=\frac{\text{The number of correctly detected people}}{\text{The number of people in a photo}}\cdot 100\% \tag{5}\]  
From (5), the higher the number of correctly detected people in a photo, the higher the value of the recall rate \(R\%\), i.e., it denotes better detection performance. Additionally, the _F_-measurement is employed to assess the overall performance of the proposed calculator, given as [25]  
\[F\text{-measure}=2\cdot\frac{R\cdot P}{R+P}\cdot 100\% \tag{6}\]  
In (6), the score of the _F_-measure is high when the scores of precision rate \(P\%\) in (4) and recall rate \(R\%\) in (5) are both high. These results indicate that the overall performance of calculating the number of people is satisfactory. The _F_-measure score decreases if the precision rate \(P\%\) or recall rate \(R\%\) score is reduced. Thus, the _F_-measure score can be utilized to evaluate the whole performance of calculating the number of people. The performance comparisons for the testing set are shown in Table 2, where the images in the testing set are not used in training the face CNN. The YOLOv4 network significantly outperforms the VJ algorithm and slightly beats the proposed app in the precision rate. The VJ algorithm's merging threshold is set to unity, enabling most face areas to be successfully detected. The recall rate is 99.53%. However, many segmented blocks without the human face are falsely detected, significantly reducing the precision rate. The face CNN can thoroughly remove the falsely detected face blocks, considerably improving the average precision rate from 36.34% to 98.61%. The recall rates obtained by the YOLOv4 network and the proposed approach are comparable. The overall performance in terms of the _F_-measure for the YOLOv4 network and proposed app can reach 98.61 and 98.27%, respectively. Accordingly, the detection accuracy of the YOLOv4 is the best, while that of the proposed app is also acceptable. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{3}{c}{**Score**} \\ \cline{2-4}  & **VJ** & **YOLOv4** & **Proposed** \\ \hline Precision rate & 36.34\% & 99.30\% & 98.61\% \\ Recall rate & 99.53\% & 97.93\% & 97.93\% \\ _F_-measure & 53.24\% & 98.61\% & 98.276\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of detection results. The mobile device is an Intel Core i5 CPU and 8 GB DDR4 memory without the help of a GPU. The performance comparison in terms of average time consumption is shown in Table 3. The YOLO v4 needs much more time (102.78 s) than the proposed app (0.09 s) in loading trained networks for face detection. It is attributed to the layer of the YOLOv4 network being very deep. On the contrary, the proposed app is relatively simple and effective. It takes only a short time to load the trained network. The YOLOv4 network needs more time (4.43 s/image) to calculate the people than the proposed app (2.55 s/image) by observing the recognition time. The YOLOv4 network obtains slightly higher recognition performance, as shown in Table 2. However, the time consumption is higher than the proposed method, as shown in Table 3, particularly the network loading time. Although the architecture of the proposed method is simple, the time consumption time for loading the network is short, while the recognition accuracy is acceptable. It is beneficial in implementing a mobile device for quickly calculating people in an image. Accordingly, the proposed approach is practical in realistic environments.