## 4 Results
The results obtained using both databases to test the system are shown in Table 2.  
As shown in Table 2, [PERSON] [28], [PERSON] [36] presented very good results because their training and test databases are very similar (see Figure 5b). In addition, the backgrounds of the images are very uniform and controlled under ideal conditions (see Figure 5c), which allows the database to be more efficient and therefore provide maximum precision. It is for this reason that the percentages of this work tend to be low in the majority of cases: the images in the training and testing databases were acquired by different people (see Figure 5d). As shown in Figure 5, [PERSON] [36] only compared identical images and did not test with other images, which is why they offered a precision of 100%. [PERSON] [28] contained images with good conditions considering the background and lighting, thus they also offered a precision of 100% but, when testing with images with low illumination, the precision reduced to 96.97%. however, they ded not present results with different gestures and under different conditions as carried out in this project. This project was tested with different images of gestures of various people in real time and with non-uniform backgrounds under different lighting conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Value**[34] & **Value**[35] \\ \hline Total Number of Gestures (Total Tested) & 20 & 20 & 20 \\ True Positive & 18 & 18 & 17 \\ True Negative & 0 & 0 & 0 \\ False Positive & 1 & 1 & 1 \\ False Negative & 1 & 1 & 2 \\ Recall & 94.74\% & 94.74\% & 89.47\% \\ Precision & 94.74\% & 94.74\% & 94.44\% \\ Prevalence & 95\% & 95\% & 95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical analysis of the proposed hand recognition system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Total Tested** & **Recall (\%)** & **Precision (\%)** & **Prevalence (\%)** \\ \hline On this project & 20 & 94.74 & 94.74 & 95 \\
[36] & 10 & 100 & 100 & 100 \\
[28] & 30 (controlled background) & 100 & 100 & 100 \\
[28] & 35 (uncontrolled background) & 96.97 & 94.12 & 94.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of the gesture recognition system of various methods. Finally, some tests are presented of the system for experimental purposes. First, a comparison of equal images was made in the training and testing databases. Only for this test, some images of the index gesture of the training and testing databases belonged to the same person. This does not mean that the databases of this system have images of the same person, and this test was only done to know the operation of the Euclidean distance with the same, similar and different images. It was observed that, when a test gesture that already exists in the training database is entered, the projection distance (i.e., the Euclidean distance) tends to be almost or completely zero; therefore, there is an encounter of the same gesture. However, when a different test gesture from the images in the training database is shown, the Euclidean distances presented may be far or close to zero. Thus, when comparing all Euclidean distances, the smallest projection distance is chosen because it corresponds to the test gesture. The above is presented in Figure 6. Figure 6a shows that the test image is the same as the training image so it presents a Euclidean distance of 0.0 \(\times\) 10\({}^{10}\). Figure 6b shows the test gesture is similar to the training gesture but is not the same, with a Euclidean distance of 0.6 \(\times\) 10\({}^{10}\). Figure 6c shows a different test gesture to the training gesture with a projection distance of 1.3 \(\times\) 10\({}^{10}\). The second test was to apply the training database of this work in an algorithm from another study. [PERSON] [37] presented a prototype system to recognize the gesture of deaf people based on ASL (American Sign Language) in order to be able to communicate with people.