# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 5 Conclusions  
Hand posture recognition is a classical task in computer vision [1; 6; 8]. Despite many methods, which perform robustly under some limitations, the problem is still exciting. Challenging barriers persist while creating a recognition system able to function in real-world conditions. The most important among them are occlusion of fingers related to the presence of affine transformation while projecting the 3D scene on the 2D image plane, scalability of considered gesture dictionaries, different background illumination, high computational cost, and repeatability of gesture execution by potential users. Currently, new devices working in the field of visible and near-infrared light are being developed, which will allow us to obtain accurate 3D information about the observed scene. There is a chance that the usage of these devices will eliminate some of the restrictions mentioned above. Therefore, in recent literature on hand gesture recognition, a shift to depth modality is observed. In this paper, a novel descriptor was proposed, which encodes information about distances between particular hand points. It is a scale-independent and distinctive alternative to other positional-based and distance-based features describing hand skeleton. Its features include relations between each fingertip and between \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Features** & **Classifier** & **Recognition Rate** \\ \hline [PERSON] et al. [15] & fingertips distances, angles, elevations & SVM-Gauss & 80.9 \\ [PERSON] et al. [45] & fingertips distances, angles, elevations + fingertips tip & SVM-Radial & 81.1 \\ [PERSON] et al. [18] & fingertips positions & SVM-Gauss & 81.5 \\ our method & PPD + DD (Euclidean) & TreeBag & 90.7 \\ our method & PPD + DD (Euclidean) & 10 NN-W & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of leave-one-subject-out, 14-fold cross-validation test results obtained for Dataset 2. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Features** & **Classifier** & **Recognition Rate** \\ \hline [PERSON] et al. [24] & angles between bones and fingers & deep networks & 35.1 \\ our method & PPD + DD (city-block) & 10 NN-W & 38.1 \\ our method & PPD + DD (city-block) & NNOR-CB & 40.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of leave-one-subject-out, 5-fold cross-validation test results obtained for the Dataset 3.  
fingertips and palm center. Unlike most other works, the method has been tested on a data set containing 48 classes, among which many similar shapes can be identified. It has been observed that the independence of the proposed approach from hand orientation is not always desirable and can lead to difficulties in recognizing some hand configurations. Therefore, a modified version of the nearest neighbor classifier was proposed, which can distinguish between very similar or identical postures, differing only in hand orientation. The n-fold cross-validation tests were performed on three challenging datasets. For each dataset, the leave-one-subject-out protocol was used, which usually gives the worst results, but is the most trustworthy. It shows how the method deals with different gesture performances by individual users. The experimental results were compared with our previous work as well as with other methods found in the literature. A significant improvement of results over the compared methods was observed. Summarizing, we did not find any methods for static hand posture recognition based only on hand skeletal data, with which we can compare our methods (mainly because of publicly available datasets) and which are better than our method in terms of recognition rate. The proposed Distance Descriptor is invariant to position and scale. It is also invariant to rotation. However, this feature is desirable only for datasets not containing classes that differ only in orientation.