## 4 Results
The video sequence was recorded on a non-uniform background and each video sequence was spatially and temporally segmented, so the training samples of each gesture are cropped video sequences, whose frames are regions of image that contain only the poses of the hand. In each database, 100 images of manual gestures were acquired. Furthermore, for the index hand posture database, the same procedure described above of the training database was performed, i.e. there are 100 images with a uniform background of 20 different people. Some images of the testing database are presented in Figure 3.  
It should be clarified that the images of the three hand postures, namely index, V sign and fist, that belong to the training database and the images of the index posture of the testing database were captured on a PC camera by 20 different subjects. However, testing the operation of the system proposed in this work involves 20 people who are different from the subjects who participated in the capture of the gestural images for the database. Figure 4 shows some results of the application of the recognition system of the three gestures (index, sign V and fist) in the hand with a paint simulation. This hand gesture recognition algorithm was implemented on the Microsoft Visual Studio 2017 Express platform with the SDK version 3.0 library. On the other hand, in Table 1, the efficiency of the gesture recognition system is shown considering the equations for the calculation of recall (Equation (12)), precision (Equation (13)) and prevalence (Equation (14)). The system was tested by 20 different subjects in real time for the detection of index, V sign and fist gestures. The results presented in Table 1 show that [PERSON] [35] presented a lower percentage than [PERSON] [34], which could be because the images in the training database have non-uniform backgrounds compared to [PERSON] [34]. \[Recall=\frac{TruePositive}{TruePositive+FalseNegative} \tag{12}\]  
\[Precision=\frac{TruePositive}{TruePositive+FalsePositive} \tag{13}\]The average execution time (this is the maximum time to recognize a gesture) of the proposed hand gesture recognition system for each gesture capture in real time is 2.6 ms. This test 3 we performed on a computer with an Intel Core 1.80 GHz processor, 4 GB RAM, a 1.3 MP camera and a resolution of 1280 \(\times\) 720 pixels. Figure 4: Detection of three types of manual gestures with painting simulation and with different backgrounds. Some images were segmented with the technique HS-ab and the other images were captured from the camera in real time. Figure 3: Gestural images of the training and testing database. Considering the results of recall, precision and prevalence of this work, a comparison was made with other methods. Some methods use the PCA algorithm for analysis in the database applied in a gesture recognition system. [PERSON] [36] presented a system that recognizes specific hand gestures to transmit information in a device control. For this, the PCA is used for manual gesture recognition and the Sheffield KInect Gesture (SKIG) dataset are extracted to create a training set. The training set consists of 10 categories of hand gestures: circle (clockwise), triangle (anti-clockwise), up-down, right-left, wave \"Z\", cross, come here, turn around and pat. These gestures are performed with three hand postures, namely fist, index and flat, under three different backgrounds, namely wooden board, plain paper and paper with characters. Table 2 shows the results of recall, precision and prevalence of 10 gesture tests in the system. [PERSON] [28] focused on a hand gesture recognition system based on skin color modeling in the YCbCr color space. Then, the image is divided into foreground and background through a defined threshold. Finally, a template based matching technique is developed using PCA. The datasets are in two forms: controlled and uncontrolled. The first has images with a similar background taken from several people. The second presents images with different lighting conditions.