## 1 Introduction  
Human gestures are non-verbal corpora actions that are used to transmit information and to express feelings. These human expressions can be done by different parts of the body; the hand is the most versatile interaction tool, and is effective fpr general use. Gestures have been classified into two categories: static and dynamic [1]. The static gestures refer to a particular pose of the hand without movement, represented by only one image and the dynamic gestures refer to the movements of the hand, represented by several images. The use of the hand gesture has become a research area in Computer Vision (CV) that plays a very important role in the field of Human-Computer Interaction (HCI). The goal of HCI is to improve the interaction between users and computers in such a way that computers are more receptive to the needs of users so that it allows humans to interact with the computer in a natural way without any physical contact with external devices such as mouse, keyboard, touch screen, joystick, etc. According to [PERSON] [2], the gesture recognition system consists of four main stages: data acquisition, segmentation and pre-processing, feature extraction, and classification. Data Acquisition is responsible for collecting the input data which are the images of the hand captured by RGB cameras, data gloves, Time-Of-Flight camera (ToF) and sensors such as Kinect. Subsequently, Segmentation prepares the image to extract the features in the next stage. Its process consists of dividing the input image into separated regions. The most commonly used technique to determine the regions of interest is the detection of skin color [3], which is used to get the skin pixels in the image. Skin color segmentation can be applied on any color space HSV, YCbCr, YUV, CIELab, etc. Part of the segmentation is Pre-processing that is responsible for image processing subject to different pre-processing techniques such as normalization, noise removal, edge detection, smoothening, and morphological operations followed by different segmentation techniques for separating the foreground from the background. After modeling, the representation of a gesture is based on the appropriate metrics with the Feature Extraction. These features can be the location of the hand, palm, and fingertips; angles of the joints and fingers; fingertip markings; number of stretched fingers; orientation; speed; histograms; any emotional expression; or body movement [4]. The extracted features are an input to a computational model, whose objective is to correctly classify the given gesture or can be stored in the system at the training stage as a model [5]. The final stage is Classification where the command/meaning of the gesture must be clearly identified by the system. A classifier allows associating each input gesture according to similarity with other test gestures (database). The input of this stage is a sequence of data (unobservable) with the parameters of a model that were acquired in the training stage. Some methods that allow classifying the gestures are: Euclidean Distance [6], Support Vector Machine (SVM) [7; 8], Clustering [9], Neural Network [10], Fuzzy Systems [11], Hidden Markov Model (HMM) [12; 13], Non-Parametric (Nearest neighbor, Minimum Distance to Centroid) [14; 15], and Principle Components Analysis (PCA) [16; 17]. PCA is a dimensionality reduction technique that can be used to solve body recognition problems. This technique is also known as Hotelling, projection of eigenvalues and eigenvectors, and Karhunen-Loeve theorem. The dimensionality reduction helps in the following way: it avoids voluminous calculations and is robust to the noise in the images. PCA transforms the original image data into a subspace set of principal components, of which the first principal component captures the greatest amount of variance among the images, while the second principal component provides the vector of the directions orthogonal to the first principal component and so on.