# A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition
## 2 Related Work  
Recently, deep networks have been developed to obtain articulated hand models called skeletons [5; 6]. These networks use color images from normal cameras and are an attractive alternative to hardware solutions requiring specialized RGB-D sensors [7; 8]. Approaches using the potential of these networks to recognize static hand configurations have appeared in the literature. Once the skeleton data is determined, various classification methods are used. There are known solutions using k-nearest neighbors classifier (KNN) [9], support vector machine (SVM) [10; 11; 12], rule-based classifier [13], artificial neural networks (ANN) [14; 15; 16; 17], random forest (RF) [12; 18], gradient boosting (GB) [11; 12], and various models based on deep learning [18; 19; 20; 15; 19; 30]. The authors of [19] used an artificially generated skeleton data to tune up a deep network trained on real samples. The authors of [15] showed that it is reasonable to train a deep network on hybrid data containing color images and skeletons. One of the challenges when recognizing finger alphabets is the high similarity of the shapes considered. To solve this problem, a hierarchical approach, in which groups of classes with similar hand shapes are identified first, has been proposed [16]. There are also descriptions of useful applications in the literature, e.g., the Sign-to-911 system, which is an emergency call service for sign language users [31], a doctor-patient dialogue system [27], or a solution supporting medical consultation [29]. Lightweight algorithms are being developed that could be run on mobile devices with limited computing resources [10]. Works are also described in which skeletal hand models are used to build educational games for learning the finger alphabet or sign language [20; 32; 33; 34]. Table 1 briefly characterizes a selection of recent works on static gesture recognition based on algorithmically determined skeletal information. For some of the works described, the table shows only that part of the results concerning static gestures. In some cases, there is no information on the number of users making gestures. In addition, the authors rarely use leave-one-subject-out validation, which verifies \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Work** & **Domain** & **Classifier** & **\# Classes** & **l-o-s-o** & **Accuracy [\%]** \\ \hline
[14] & Thai Finger Alphabet & ANN & 30 & no & 84.57 \\
[19] & Irish Finger Alphabet & CNN & 23 & no & 71.00 \\
[10] & Japanese Finger Alphabet & SVM & 24 & no & 100.00 \\
[18] & Arabic Finger Alphabet & RF & 30 & no & 99.70 \\  & + control signs & & & & \\
[11] & American Finger Alphabet & GB & 26 & no & 99.39 \\
[20] & Static gestures from & ResNet2+1D & 36 & yes & 96.20 \\  & Greek Sign Language & & & & \\
[35] & American Finger Alphabet & SVM & 51 & no & 98.98 \\  & + numbers and words & & & & \\
[16] & American Finger Alphabet & ANN & 29 & no & 94.07 \\  & + control signs & & & & \\
[36] & American Finger Alphabet & ANN & 38 & no & 94.29 \\  & + static gestures & & & & \\
[13] & Japanese Finger Alphabet & rule set & 46 & no & 52.83 \\
[22] & Static gestures from & CNN & 15 & no & 98.90 \\  & Indian Sign Language & & & & \\
[12] & Kazakh Finger Alphabet & RF, SVM, GB & 31 & no & 98.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent works on static gesture recognition based on algorithmically determined skeletons. the reliability of the method for gestures shown by people who are not present in the training dataset. Solutions based on deep learning dominate. However, a recent review article [37] points out the lack of comprehensive, representative, and annotated datasets, especially for languages other than American Sign Language (ASL). Therefore, training deep models from scratch is tedious and expensive. With this in mind, it is worth turning to solutions that use the potential of trained networks generating skeleton data, require only fine-tuning using a limited training set, and use domain knowledge.