# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 5 Discussion  
The simulation results show that the proposed method outperformed the JRNN. Difficulty of this gesture data set is that class pairs 1-2, 4-5, 7-8 are reverse gestures. Appearance of poses in the gesture is reverse order and those pairs include the same hand poses. For classes 1 and 2, the proposed system is inferior to the JRNN, but the proposed method recognized class 5 better. Another noticeable point is that the recognition accuracies of the JRNN for classes 6 and 9 are significantly worse than those of the proposed system. This is caused by the another difficulty of the data set. As shown in Figure 4, the class pairs 4-6 and 7-9 have the same poses in their beginning, which confuse the classifiers. The experimental results shown in Tables 2 and 3 disclose that the recognition and spotting performances of the proposed system are very high. Regarding the spotting, the spotting may be easily implemented by counting the frames because the number of frames of gestures are fixed. To do so, the start of gesture must be detected correctly, and a possible method is the use of key poses to indicate the start of gestures, which was used in [15; 21]. However, these approaches disturb the natural flow of gesture. Meanwhile the proposed spotting finds the end of gesture automatically when the sequence of frames matches one of the pre-trained ones, therefore the user can start gesture at any time without the key poses. The tables also indicate that the most of the recognition errors were caused by the spotting errors. In case of NS, no spotting was detected, and the recognition result was not available. During the experiment, we observed that the proposed spotting detected the end of gesture twice in all MS cases, and two recognition results were outputted. In most of such cases, recognition results at the first spotting were incorrect and the second ones were correct. Therefore, if the detection accuracy of the spotting function is more precise, recognition results would be improved. Table 4 compares the recognition accuracies of the proposed system with the state-of-the-art in the literature. Since experimental conditions are not the same, the accuracies in the table should not be directly compared. Six of them are real-time recognition systems, and the others were tested with various gesture data sets. Four of them are vision based systems, and the others used 3D gesture images taken from the special sensors. Vision based system is more challenging than the 3D gesture recognition since it uses limited 2D information, but it can be realized with the simple readily available cameras. Additional burdens of the real-time recognition system are high speed computation and the gesture spotting. Note that the proposed system provides the natural spotting function with no special key posture that indicates the start or end of gesture. Considering features of vision-based, real-time accurate gesture recognition and spotting function, the overall contribution of the proposed system in dynamic gesture recognition application is very high. However, even though the proposed system does not require the special sensors, it \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{8}{c}{**Recognition Results**} & \multicolumn{3}{c}{**Spotting**} & **Accuracy** \\ \cline{2-11}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **NS** & **MS** & **\%** \\ \hline
1 & 93 & & & & & & & & & 7 & & 93 \\
2 & & 95 & & & 3 & & & & & 2 & & 95 \\
3 & & & 98 & & 1 & 1 & & & & & 98 \\
4 & 2 & & & 94 & & & & & & 1 & 3 & 94 \\
5 & & & & & 100 & & & & & & & 100 \\
6 & & & & & 1 & 99 & & & & & & 99 \\
7 & & & & & & & 95 & & & 3 & 2 & 95 \\
8 & & & & & & & & 99 & & 1 & & 99 \\
9 & & & & & & & & & 100 & & & 100 \\  & & & & & & & & & & Average & 97.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion table (10 fps). still requires users to wear the color glove, which may prevent it being used in everyday life.