# Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier  
[PERSON]  
1 Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia; [EMAIL_ADDRESS] (E.L.R.E.); [EMAIL_ADDRESS] (L.C.K.)  
[PERSON]  
2 Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia; 2 [EMAIL_ADDRESS] (E.L.R.E.); lckwek@mmu.  
hand gestures are made up of various forms and hand orientations that do not reflect any motion information.  
The vision-based approach can be divided into two categories, handcrafted machine learning approach or deep learning approach (depicted in Figure 1). The handcrafted approach, which is also known as the traditional machine learning, has a separate section for its features defined and extracted prior subjecting it through the machine learning algorithm. Some examples of features that are pre-defined are edge detection, corner detection, histograms, etc. On the other hand, the deep learning approach does not need a specific manual feature extraction process as the algorithm itself basically searches for what features are best to classify the images, such as CNN. The major difference between deep learning and machine learning techniques is the problem-solving approach. Deep learning techniques tend to solve the problem end to end, whereas machine learning techniques need to break down the problem statements into different parts to be solved first; then, their results are combined in the final stage.  
Lately, more studies have been carried out to propose a model which can classify datasets of different conditions such as the illuminations level and complex backgrounds through CNN. By employing CNN, the hand-crafted feature extraction portion can be avoided, especially when the dataset comes with complex backgrounds. However, whenever it involves CNN, the dataset size is one of the crucial considerations when it comes to classification. Generally, deep neural networks require a very large amount of training data to avoid overfitting, whereas traditional machine learning approaches are more robust due to their hierarchical structure and have a shorter execution time. In order to achieve a better accuracy, researchers tried to perform a deeper convolution layer but have reported that computation resources such as computer memory is a major stumbling block, not to also mention the time taken to perform training.  
In this paper, a hybrid hand gesture recognition model based on CNN as part of the deep learning and ensemble classifier is introduced. The performance of a model heavily depends on the features studied and extracted accurately. Hence, feature extraction via the CNN approach avoids complex methods in manual feature extraction, especially when it is required to be crafted according to each individual dataset. However, the dataset size and execution time, which have constantly been a source of worry with regard to CNN, will be addressed using machine learning methods in classification. This paper draws several key contributions as follows:  
1. A hybrid model using deep learning techniques for feature extraction and an ensemble classifier for classification (Lightweight VGG16 and Random Forest) is devised for hand gesture recognition;
2. Reduced burden on the computation resources required for VGG16 feature extraction through architecture depth optimization;
3. Execution time improvement in the comparison of lightweight VGG16-RF to a full-fledged deep learning architecture for hand gesture recognition.  
Figure 1: Different categories of vision-based approaches.  
What remains of this paper is organized as follows: Section 2 reviews the related work pertaining to hand gesture recognition; Section 3 presents the proposed model; Section 4 covers the datasets used, the experiments carried out and the results recorded; and Section 5 concludes the paper.