# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 5 Conclusions
Using the proposed nearest neighbor classifier with orientation restriction makes the recognition method partially dependent on orientation, enabling it to distinguish between such gestures. The threshold parameter \(\gamma_{t}\) has to be experimentally set based on the orientation of similar postures of the considered sign language; it should be less than the least angle between palm normals of any similar postures classes. However, it also cannot be too small, since that would make the recognition method too strongly dependent on hand orientation. It is worth noting that descriptors and features proposed in the literature are not always invariant to hand size and orientation. The features of PPD contain the information about angular relations of skeletal joints. The information of DD features is positional and relies on distance between the joints. The results from Tables 3 and 5 show that the angular information of PPD and the positional information of DD complement each other, since the addition of DD to PPD features significantly improved the recognition rate. The most commonly confused hand postures of the Dataset 1 are B-Bm, C-100, S-F, T-O, Z-Xm, Bz-Cm, and 4z-Cm. These postures are shown in pairs in Figure 3 Most of these hand-shapes are very similar, even for the human eye. The proposed recognition method is fast and does not require a specific background, lighting conditions, or any special outfit--e.g., gloves. The main reason for the weaker results of leave-one-subject-out tests is the imperfection of the sensor, which has issues with proper detection of occluding fingers. Therefore, further work may include obtaining more accurate and reliable hand skeletal data using two calibrated depth sensors. Another future study topic may be recognition  
Figure 3: The most often confused postures of Dataset 1.  
of letter sequences (finger spelling), understood as quick, highly coarticulated motions. Finally, the developed PPD and DD descriptors can be adopted in a method for recognition of human actions based on whole-body skeletons (e.g., obtained from Kinect camera). Conceptualization, methodology, D.W. and T.K. ; software, D.W. and T.K. ; datasets, T.K. ; experiments design, discussion of the results, D.W.; writing--review and editing, D.W. and T.K. All authors have read and agreed to the published version of the manuscript. This project is financed by the Minister of Science and Higher Education of the Republic of Poland within the \"Regional Initiative of Excellence\" program for years 2019-2022. Project number 027/RID/2018/19, amount granted 11 999 900 PLN. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} PPD & Point Pair Descriptor \\ DD & Distance Descriptor \\ SVM-Lin & support vector machine with linear kernel function \\ LD & linear discriminant \\ TreeBag & ensemble of multiple decision trees--tree bagger \\
10 NN-W & weighted k-nearest neighbors classifier with \(k=10\) \\ NNOR-CB & nearest neighbor with orientation restriction and city block distance \\ NNOR-Euc & nearest neighbor with orientation restriction and Euclidean distance \\ \end{tabular}