## 3 Methodology
### Data Acquisition  
In this study, we focused on the following datasets:  
* The EgoGesture dataset: The data collected by the authors of [38] are from an egocentric point of view and consist of both static and dynamic gestures.
* The NVGesture dataset: This also offers static as well as dynamic gestures. This dataset was intended to be used for driver-control applications [39]. The subjects  
Figure 2: Overview of the proposed pipeline for hand gesture recognition in real-time drone control. The pipeline consists of dataset preprocessing, hand landmark detection using the MHL model, feature extraction, model training with lightweight architectures, and performance evaluation through subjective and objective metrics.  
in this dataset are limited to a contained space where performing dynamic gestures becomes critical for smooth communication.
* The Jester Dataset: This was created for use in action recognition and other HCI interaction-based systems [40].
* The sign-language dataset: This is the fourth dataset in our study and it is also a static and dynamic gesture dataset with a specific emphasis on sign-language gestures. Beyond traditional communication, this dataset plays a crucial role in facilitating communication for individuals with hearing impairments. Sign-language recognition systems play a vital role in the development of technologies that enhance vastness and accessibility [42].  
These datasets are well suited for HGR, as prior studies [38; 39; 40; 42] provide a solid background for evaluating the performance of transfer learning models.  
We extracted 12 classes from each of the datasets and formed subsets (Subset-EgoGesture, Subset-NVGesture, Subset-Jester, and Subset-sign-language) for our evaluation experiments and analysis. Each class extracted from its original dataset retained the exact same number of samples as provided by the datasets to ensure consistency in the analysis. Table 1 provides an overview of these benchmark datasets, and Figure 3 shows sample gestures from each dataset.  
The Egogesture dataset provides a diverse array of static and dynamic gestures captured from an egocentric viewpoint. It includes 83 classes, primarily using right-hand gestures at a steady distance from the camera. The NVgesture dataset is substantial and diverse with 27 gesture classes. This dataset also uses right-hand gestures at a uniform distance from the camera. It includes gestures performed by various subjects, offering a range of gesture variations within each class. The Jester dataset is designed for action/gesture recognition (HGR) and human-computer interaction (HCI). It includes images with a resolution of \(120\times 100\) pixels and captures various gestures performed by different subjects. The sign-language dataset demonstrates unparalleled accuracy in gesture recognition tasks.  
Figure 3: Visual representation of sample images from four benchmark datasets, showcasing diverse data characteristics utilized in this study for hand gesture recognition. (**a**) EgoGesture. (**b**) NVGesture. (**c**) Jester. (**d**) Sign-language.  
By focusing on essential gestures, this dataset is highly effective in sign-language interpretation applications. Table 1 reports the total number of samples for each of the datasets, the total number of classes, statistics showing the standard deviation of the gesture clips, the resolution, and the viewpoint of the data. In the last column, it mentions the biases in the datasets (hand gestures recorded with one or both hands). The sign-language dataset was designed and recorded using both hands, while the rest of the datasets are mostly biased (right-handed).